Artificial Intelligence Nanodegree

Computer Vision Capstone

Project: Facial Keypoint Detection


Welcome to the final Computer Vision project in the Artificial Intelligence Nanodegree program!

In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system! Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.

There are three main parts to this project:

Part 1 : Investigating OpenCV, pre-processing, and face detection

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!


*Here's what you need to know to complete the project:

  1. In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested.

    a. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

  1. In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation.

    a. Each section where you will answer a question is preceded by a 'Question X' header.

    b. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional suggestions for enhancing the project beyond the minimum requirements. If you decide to pursue the "(Optional)" sections, you should include the code in this IPython notebook.

Your project submission will be evaluated based on your answers to each of the questions and the code implementations you provide.

Steps to Complete the Project

Each part of the notebook is further broken down into separate steps. Feel free to use the links below to navigate the notebook.

In this project you will get to explore a few of the many computer vision algorithms built into the OpenCV library. This expansive computer vision library is now almost 20 years old and still growing!

The project itself is broken down into three large parts, then even further into separate steps. Make sure to read through each step, and complete any sections that begin with '(IMPLEMENTATION)' in the header; these implementation sections may contain multiple TODOs that will be marked in code. For convenience, we provide links to each of these steps below.

Part 1 : Investigating OpenCV, pre-processing, and face detection

  • Step 0: Detect Faces Using a Haar Cascade Classifier
  • Step 1: Add Eye Detection
  • Step 2: De-noise an Image for Better Face Detection
  • Step 3: Blur an Image and Perform Edge Detection
  • Step 4: Automatically Hide the Identity of an Individual

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

  • Step 5: Create a CNN to Recognize Facial Keypoints
  • Step 6: Compile and Train the Model
  • Step 7: Visualize the Loss and Answer Questions

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!

  • Step 8: Build a Robust Facial Keypoints Detector (Complete the CV Pipeline)

Step 0: Detect Faces Using a Haar Cascade Classifier

Have you ever wondered how Facebook automatically tags images with your friends' faces? Or how high-end cameras automatically find and focus on a certain person's face? Applications like these depend heavily on the machine learning task known as face detection - which is the task of automatically finding faces in images containing people.

At its root face detection is a classification problem - that is a problem of distinguishing between distinct classes of things. With face detection these distinct classes are 1) images of human faces and 2) everything else.

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the detector_architectures directory.

Import Resources

In the next python cell, we load in the required libraries for this section of the project.

In [1]:
# Import required libraries for this section

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
import math
import cv2                     # OpenCV library for computer vision
from PIL import Image
import time 

Next, we load in and display a test image for performing face detection.

Note: by default OpenCV assumes the ordering of our image's color channels are Blue, then Green, then Red. This is slightly out of order with most image types we'll use in these experiments, whose color channels are ordered Red, then Green, then Blue. In order to switch the Blue and Red channels of our test image around we will use OpenCV's cvtColor function, which you can read more about by checking out some of its documentation located here. This is a general utility function that can do other transformations too like converting a color image to grayscale, and transforming a standard color image to HSV color space.

In [2]:
# Load in color image for face detection
image = cv2.imread('images/test_image_1.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot our image using subplots to specify a size and title
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[2]:
<matplotlib.image.AxesImage at 0x21b8cb3db00>

There are a lot of people - and faces - in this picture. 13 faces to be exact! In the next code cell, we demonstrate how to use a Haar Cascade classifier to detect all the faces in this test image.

This face detector uses information about patterns of intensity in an image to reliably detect faces under varying light conditions. So, to use this face detector, we'll first convert the image from color to grayscale.

Then, we load in the fully trained architecture of the face detector -- found in the file haarcascade_frontalface_default.xml - and use it on our image to find faces!

To learn more about the parameters of the detector see this post.

In [3]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[3]:
<matplotlib.image.AxesImage at 0x21b8d92f4a8>

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.


Step 1: Add Eye Detections

There are other pre-trained detectors available that use a Haar Cascade Classifier - including full human body detectors, license plate detectors, and more. A full list of the pre-trained architectures can be found here.

To test your eye detector, we'll first read in a new test image with just a single face.

In [4]:
# Load in color image for face detection
image = cv2.imread('images/james.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot the RGB image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[4]:
<matplotlib.image.AxesImage at 0x21b8d98d860>

Notice that even though the image is a black and white image, we have read it in as a color image and so it will still need to be converted to grayscale in order to perform the most accurate face detection.

So, the next steps will be to convert this image to grayscale, then load OpenCV's face detector and run it with parameters that detect this face accurately.

In [5]:
# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

def draw_face(image, trace=True):
    # Convert the RGB  image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

    # Detect the faces in image
    faces = face_cascade.detectMultiScale(gray, 1.25, 6)

    # Print the number of faces detected in the image
    if trace:
        print('Number of faces detected:', len(faces))

    # Make a copy of the orginal image to draw face detections on
    image_with_detections = np.copy(image)

    # Get the bounding box for each detected face
    for (x,y,w,h) in faces:
        # Add a red bounding box to the detections image
        cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    
    return image_with_detections, faces, gray
    
image_with_detections, faces, gray = draw_face(image, trace=True)

# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 1
Out[5]:
<matplotlib.image.AxesImage at 0x21ba22e5780>

(IMPLEMENTATION) Add an eye detector to the current face detection setup.

A Haar-cascade eye detector can be included in the same way that the face detector was and, in this first task, it will be your job to do just this.

To set up an eye detector, use the stored parameters of the eye cascade detector, called haarcascade_eye.xml, located in the detector_architectures subdirectory. In the next code cell, create your eye detector and store its detections.

A few notes before you get started:

First, make sure to give your loaded eye detector the variable name

eye_cascade

and give the list of eye regions you detect the variable name

eyes

Second, since we've already run the face detector over this image, you should only search for eyes within the rectangular face regions detected in faces. This will minimize false detections.

Lastly, once you've run your eye detector over the facial detection region, you should display the RGB image with both the face detection boxes (in red) and your eye detections (in green) to verify that everything works as expected.

In [6]:
# Make a copy of the original image to plot rectangle detections
image_with_detections = np.copy(image)   

# Loop over the detections and draw their corresponding face detection boxes
for (x,y,w,h) in faces:
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  
    
# Do not change the code above this comment!
    
## TODO: Add eye detection, using haarcascade_eye.xml, to the current face detector algorithm
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')

def draw_eyes(image_with_detections, gray, faces):
    ## TODO: Loop over the eye detections and draw their corresponding boxes in green on image_with_detections
    for (x,y,w,h) in faces:
        eyes = eye_cascade.detectMultiScale(gray[y:y+h, x:x+w], 1.1, 6)
        for (xe,ye,we,he) in eyes:
            cv2.rectangle(image_with_detections, (xe+x,ye+y), (xe+x+we,ye+y+he),(0,255,0), 3)  
    
    return image_with_detections
    
image_with_detections = draw_eyes(image_with_detections, gray, faces)

# Plot the image with both faces and eyes detected
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face and Eye Detection')
ax1.imshow(image_with_detections)
Out[6]:
<matplotlib.image.AxesImage at 0x21ba24e8668>

(Optional) Add face and eye detection to your laptop camera

It's time to kick it up a notch, and add face and eye detection to your laptop's camera! Afterwards, you'll be able to show off your creation like in the gif shown below - made with a completed version of the code!

Notice that not all of the detections here are perfect - and your result need not be perfect either. You should spend a small amount of time tuning the parameters of your detectors to get reasonable results, but don't hold out for perfection. If we wanted perfection we'd need to spend a ton of time tuning the parameters of each detector, cleaning up the input image frames, etc. You can think of this as more of a rapid prototype.

The next cell contains code for a wrapper function called laptop_camera_face_eye_detector that, when called, will activate your laptop's camera. You will place the relevant face and eye detection code in this wrapper function to implement face/eye detection and mark those detections on each image frame that your camera captures.

Before adding anything to the function, you can run it to get an idea of how it works - a small window should pop up showing you the live feed from your camera; you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [7]:
### Add face and eye detection to this laptop camera function 
# Make sure to draw out all faces/eyes found in each frame on the shown video feed

import cv2
import time 

def plot_faces_and_eyes(image):
    image_with_detections, faces, gray = draw_face(image, trace=False)
    image_with_detections = draw_eyes(image_with_detections, gray, faces)
    return image_with_detections

# wrapper function for face/eye detection with your laptop camera
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)
    
    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep the video stream open
    while rval:
        # Plot the image from camera with all the face and eye detections marked
        frame = plot_faces_and_eyes(frame)
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key == 27: # Exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            # Make sure window closes on OSx
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
In [8]:
# Call the laptop camera face/eye detector function above
laptop_camera_go()

Step 2: De-noise an Image for Better Face Detection

Image quality is an important aspect of any computer vision task. Typically, when creating a set of images to train a deep learning network, significant care is taken to ensure that training images are free of visual noise or artifacts that hinder object detection. While computer vision algorithms - like a face detector - are typically trained on 'nice' data such as this, new test data doesn't always look so nice!

When applying a trained computer vision algorithm to a new piece of test data one often cleans it up first before feeding it in. This sort of cleaning - referred to as pre-processing - can include a number of cleaning phases like blurring, de-noising, color transformations, etc., and many of these tasks can be accomplished using OpenCV.

In this short subsection we explore OpenCV's noise-removal functionality to see how we can clean up a noisy image, which we then feed into our trained face detector.

Create a noisy image to work with

In the next cell, we create an artificial noisy version of the previous multi-face image. This is a little exaggerated - we don't typically get images that are this noisy - but image noise, or 'grainy-ness' in a digitial image - is a fairly common phenomenon.

In [9]:
# Load in the multi-face test image again
image = cv2.imread('images/test_image_1.jpg')

# Convert the image copy to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Make an array copy of this image
image_with_noise = np.asarray(image)

# Create noise - here we add noise sampled randomly from a Gaussian distribution: a common model for noise
noise_level = 40
noise = np.random.randn(image.shape[0],image.shape[1],image.shape[2])*noise_level

# Add this noise to the array image copy
image_with_noise = image_with_noise + noise

# Convert back to uint8 format
image_with_noise = np.asarray([np.uint8(np.clip(i,0,255)) for i in image_with_noise])

# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image')
ax1.imshow(image_with_noise)
Out[9]:
<matplotlib.image.AxesImage at 0x21ba2005668>

In the context of face detection, the problem with an image like this is that - due to noise - we may miss some faces or get false detections.

In the next cell we apply the same trained OpenCV detector with the same settings as before, to see what sort of detections we get.

In [10]:
# Convert the RGB  image to grayscale
gray_noise = cv2.cvtColor(image_with_noise, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_noise, 2, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_with_noise)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 12
Out[10]:
<matplotlib.image.AxesImage at 0x21ba2170ac8>

With this added noise we now miss one of the faces!

(IMPLEMENTATION) De-noise this image for better face detection

Time to get your hands dirty: using OpenCV's built in color image de-noising functionality called fastNlMeansDenoisingColored - de-noise this image enough so that all the faces in the image are properly detected. Once you have cleaned the image in the next cell, use the cell that follows to run our trained face detector over the cleaned image to check out its detections.

You can find its official documentation here and a useful example here.

Note: you can keep all parameters except photo_render fixed as shown in the second link above. Play around with the value of this parameter - see how it affects the resulting cleaned image.

In [11]:
## TODO: Use OpenCV's built in color image de-noising function to clean up our noisy image!

# your final de-noised image (should be RGB)
denoised_image = cv2.fastNlMeansDenoisingColored(image_with_noise, h=19, hColor=19, templateWindowSize=5, searchWindowSize=15)

# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Denoised Image')
ax1.imshow(denoised_image)
Out[11]:
<matplotlib.image.AxesImage at 0x21ba21d0160>
In [12]:
## TODO: Run the face detector on the de-noised image to improve your detections and display the result

# Convert the RGB  image to grayscale
gray_denoised_image = cv2.cvtColor(denoised_image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_denoised_image, 2, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(denoised_image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[12]:
<matplotlib.image.AxesImage at 0x21ba2224a90>

Step 3: Blur an Image and Perform Edge Detection

Now that we have developed a simple pipeline for detecting faces using OpenCV - let's start playing around with a few fun things we can do with all those detected faces!

Importance of Blur in Edge Detection

Edge detection is a concept that pops up almost everywhere in computer vision applications, as edge-based features (as well as features built on top of edges) are often some of the best features for e.g., object detection and recognition problems.

Edge detection is a dimension reduction technique - by keeping only the edges of an image we get to throw away a lot of non-discriminating information. And typically the most useful kind of edge-detection is one that preserves only the important, global structures (ignoring local structures that aren't very discriminative). So removing local structures / retaining global structures is a crucial pre-processing step to performing edge detection in an image, and blurring can do just that.

Below is an animated gif showing the result of an edge-detected cat taken from Wikipedia, where the image is gradually blurred more and more prior to edge detection. When the animation begins you can't quite make out what it's a picture of, but as the animation evolves and local structures are removed via blurring the cat becomes visible in the edge-detected image.

Edge detection is a convolution performed on the image itself, and you can read about Canny edge detection on this OpenCV documentation page.

Canny edge detection

In the cell below we load in a test image, then apply Canny edge detection on it. The original image is shown on the left panel of the figure, while the edge-detected version of the image is shown on the right. Notice how the result looks very busy - there are too many little details preserved in the image before it is sent to the edge detector. When applied in computer vision applications, edge detection should preserve global structure; doing away with local structures that don't help describe what objects are in the image.

In [13]:
# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[13]:
<matplotlib.image.AxesImage at 0x21ba22a15c0>

Without first blurring the image, and removing small, local structures, a lot of irrelevant edge content gets picked up and amplified by the detector (as shown in the right panel above).

(IMPLEMENTATION) Blur the image then perform edge detection

In the next cell, you will repeat this experiment - blurring the image first to remove these local structures, so that only the important boudnary details remain in the edge-detected image.

Blur the image by using OpenCV's filter2d functionality - which is discussed in this documentation page - and use an averaging kernel of width equal to 4.

In [14]:
### TODO: Blur the test imageusing OpenCV's filter2d functionality, 
# Use an averaging kernel, and a kernel width equal to 4
kernel_size = 4
kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size ** 2)
filtered = cv2.filter2D(gray, -1, kernel)

## TODO: Then perform Canny edge detection and display the output
edges2 = cv2.Canny(filtered, 100, 200)
edges2 = cv2.dilate(edges2, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(221)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(gray, cmap='gray')

ax2 = fig.add_subplot(222)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')

ax3 = fig.add_subplot(223)
ax3.set_xticks([])
ax3.set_yticks([])

ax3.set_title('Blurred Image')
ax3.imshow(filtered, cmap='gray')

ax4 = fig.add_subplot(224)
ax4.set_xticks([])
ax4.set_yticks([])

ax4.set_title('Blurred canny Edges')
ax4.imshow(edges2, cmap='gray')
Out[14]:
<matplotlib.image.AxesImage at 0x21ba246bba8>

Step 4: Automatically Hide the Identity of an Individual

If you film something like a documentary or reality TV, you must get permission from every individual shown on film before you can show their face, otherwise you need to blur it out - by blurring the face a lot (so much so that even the global structures are obscured)! This is also true for projects like Google's StreetView maps - an enormous collection of mapping images taken from a fleet of Google vehicles. Because it would be impossible for Google to get the permission of every single person accidentally captured in one of these images they blur out everyone's faces, the detected images must automatically blur the identity of detected people. Here's a few examples of folks caught in the camera of a Google street view vehicle.

Read in an image to perform identity detection

Let's try this out for ourselves. Use the face detection pipeline built above and what you know about using the filter2D to blur and image, and use these in tandem to hide the identity of the person in the following image - loaded in and printed in the next cell.

In [15]:
# Load in the image
image = cv2.imread('images/gus.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[15]:
<matplotlib.image.AxesImage at 0x21ba2580780>

(IMPLEMENTATION) Use blurring to hide the identity of an individual in an image

The idea here is to 1) automatically detect the face in this image, and then 2) blur it out! Make sure to adjust the parameters of the averaging blur filter to completely obscure this person's identity.

In [16]:
## TODO: Implement face detection
def get_face_detector():
    face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')
    return face_cascade

def detect_faces(image, detector):
    faces = detector.detectMultiScale(image, 2, 6)
    return faces

def blur_regions(image, regions):
    
    blured_image = np.copy(image)
    kernel_size = 45
    kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size ** 2)
    
    for (x, y, w, h) in regions:
        blured_region = np.copy(image[y : y + h, x : x + w, :])
        blured_region = cv2.filter2D(blured_region, -1, kernel)
        blured_image[y : y + h, x : x + w, :] = blured_region 

    return blured_image

def blur_pipeline(image):
    detector = get_face_detector()
    faces = detect_faces(image, detector)
    blured_image = blur_regions(image, faces)
    return blured_image 

## TODO: Blur the bounding box around each detected face using an averaging filter and display the result
res = blur_pipeline(image)

fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Blured Image')
ax1.imshow(res)
Out[16]:
<matplotlib.image.AxesImage at 0x21ba25e01d0>

(Optional) Build identity protection into your laptop camera

In this optional task you can add identity protection to your laptop camera, using the previously completed code where you added face detection to your laptop camera - and the task above. You should be able to get reasonable results with little parameter tuning - like the one shown in the gif below.

As with the previous video task, to make this perfect would require significant effort - so don't strive for perfection here, strive for reasonable quality.

The next cell contains code a wrapper function called laptop_camera_identity_hider that - when called - will activate your laptop's camera. You need to place the relevant face detection and blurring code developed above in this function in order to blur faces entering your laptop camera's field of view.

Before adding anything to the function you can call it to get a hang of how it works - a small window will pop up showing you the live feed from your camera, you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [17]:
### Insert face detection and blurring code into the wrapper below to create an identity protector on your laptop!
import cv2
import time 

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        frame = blur_pipeline(frame)
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key == 27: # Exit by pressing any key
            # Destroy windows
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [18]:
# Run laptop identity hider
laptop_camera_go()

Step 5: Create a CNN to Recognize Facial Keypoints

OpenCV is often used in practice with other machine learning and deep learning libraries to produce interesting results. In this stage of the project you will create your own end-to-end pipeline - employing convolutional networks in keras along with OpenCV - to apply a "selfie" filter to streaming video and images.

You will start by creating and then training a convolutional network that can detect facial keypoints in a small dataset of cropped images of human faces. We then guide you towards OpenCV to expanding your detection algorithm to more general images. What are facial keypoints? Let's take a look at some examples.

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.

Below we illustrate a filter that, using the results of this section, automatically places sunglasses on people in images (using the facial keypoints to place the glasses correctly on each face). Here, the facial keypoints have been colored lime green for visualization purposes.

Make a facial keypoint detector

But first things first: how can we make a facial keypoint detector? Well, at a high level, notice that facial keypoint detection is a regression problem. A single face corresponds to a set of 15 facial keypoints (a set of 15 corresponding $(x, y)$ coordinates, i.e., an output point). Because our input data are images, we can employ a convolutional neural network to recognize patterns in our images and learn how to identify these keypoint given sets of labeled data.

In order to train a regressor, we need a training set - a set of facial image / facial keypoint pairs to train on. For this we will be using this dataset from Kaggle. We've already downloaded this data and placed it in the data directory. Make sure that you have both the training and test data files. The training dataset contains several thousand $96 \times 96$ grayscale images of cropped human faces, along with each face's 15 corresponding facial keypoints (also called landmarks) that have been placed by hand, and recorded in $(x, y)$ coordinates. This wonderful resource also has a substantial testing set, which we will use in tinkering with our convolutional network.

To load in this data, run the Python cell below - notice we will load in both the training and testing sets.

The load_data function is in the included utils.py file.

In [19]:
from utils import *

# Load training set
X_train, y_train = load_data()
print("X_train.shape == {}".format(X_train.shape))
print("y_train.shape == {}; y_train.min == {:.3f}; y_train.max == {:.3f}".format(
    y_train.shape, y_train.min(), y_train.max()))

# Load testing set
X_test, _ = load_data(test=True)
print("X_test.shape == {}".format(X_test.shape))
Using TensorFlow backend.
X_train.shape == (2140, 96, 96, 1)
y_train.shape == (2140, 30); y_train.min == -0.920; y_train.max == 0.996
X_test.shape == (1783, 96, 96, 1)

The load_data function in utils.py originates from this excellent blog post, which you are strongly encouraged to read. Please take the time now to review this function. Note how the output values - that is, the coordinates of each set of facial landmarks - have been normalized to take on values in the range $[-1, 1]$, while the pixel values of each input point (a facial image) have been normalized to the range $[0,1]$.

Note: the original Kaggle dataset contains some images with several missing keypoints. For simplicity, the load_data function removes those images with missing labels from the dataset. As an optional extension, you are welcome to amend the load_data function to include the incomplete data points.

Visualize the Training Data

Execute the code cell below to visualize a subset of the training data.

In [20]:
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_train[i], y_train[i], ax)

For each training image, there are two landmarks per eyebrow (four total), three per eye (six total), four for the mouth, and one for the tip of the nose.

Review the plot_data function in utils.py to understand how the 30-dimensional training labels in y_train are mapped to facial locations, as this function will prove useful for your pipeline.

(IMPLEMENTATION) Specify the CNN Architecture

In this section, you will specify a neural network for predicting the locations of facial keypoints. Use the code cell below to specify the architecture of your neural network. We have imported some layers that you may find useful for this task, but if you need to use more Keras layers, feel free to import them in the cell.

Your network should accept a $96 \times 96$ grayscale image as input, and it should output a vector with 30 entries, corresponding to the predicted (horizontal and vertical) locations of 15 facial keypoints. If you are not sure where to start, you can find some useful starting architectures in this blog, but you are not permitted to copy any of the architectures that you find online.

In [21]:
# Import deep learning resources from Keras
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Dropout, Activation, BatchNormalization
from keras.layers import Flatten, Dense


## TODO: Specify a CNN architecture
# Your model should accept 96x96 pixel graysale images in
# It should have a fully-connected output layer with 30 values (2 for each facial keypoint)

entities_count = 30

def getModel10():
    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='same',
                    input_shape=(96, 96, 1), use_bias=True))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Flatten())
    model.add(Dense(512, activation="relu"))
    model.add(Dense(entities_count))

    return model

def getModel20():
    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='same',
                    input_shape=(96, 96, 1), use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Flatten())
    model.add(Dense(1024, activation="relu"))
    model.add(Dropout(0.25))
    model.add(Dense(512, activation="relu"))
    model.add(Dropout(0.25))
    model.add(Dense(entities_count))

    return model

def getModel30():
    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='same',
                     input_shape=(96, 96, 1), use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=256, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=False))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Flatten())
    model.add(Dense(1024, activation="relu"))
    model.add(Dropout(0.25))
    model.add(Dense(512, activation="relu"))
    model.add(Dropout(0.25))
    model.add(Dense(entities_count))

    return model

models = {}
models[1] = (getModel10(), None, 'SGD', 'Simple Model')
models[2] = (getModel20(), None, 'SGD', 'Intermediate Model')
models[3] = (getModel30(), None, 'SGD', 'Complex Model')
models[4] = (getModel10(), None, 'rmsprop', 'Simple Model')
models[5] = (getModel20(), None, 'rmsprop', 'Intermediate Model')
models[6] = (getModel30(), None, 'rmsprop', 'Complex Model')
models[7] = (getModel10(), None, 'adam', 'Simple Model')
models[8] = (getModel20(), None, 'adam', 'Intermediate Model')
models[9] = (getModel30(), None, 'adam', 'Complex Model')
models[10] = (getModel10(), None, 'adagrad', 'Simple Model')
models[11] = (getModel20(), None, 'adagrad', 'Intermediate Model')
models[12] = (getModel30(), None, 'adagrad', 'Complex Model')
models[13] = (getModel10(), None, 'nadam', 'Simple Model')
models[14] = (getModel20(), None, 'nadam', 'Intermediate Model')
models[15] = (getModel30(), None, 'nadam', 'Complex Model')

# Summarize the model
#model.summary()

Step 6: Compile and Train the Model

After specifying your architecture, you'll need to compile and train the model to detect facial keypoints'

(IMPLEMENTATION) Compile and Train the Model

Use the compile method to configure the learning process. Experiment with your choice of optimizer; you may have some ideas about which will work best (SGD vs. RMSprop, etc), but take the time to empirically verify your theories.

Use the fit method to train the model. Break off a validation set by setting validation_split=0.2. Save the returned History object in the history variable.

Your model is required to attain a validation loss (measured as mean squared error) of at least XYZ. When you have finished training, save your model as an HDF5 file with file path my_model.h5.

In [22]:
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam

epochs = 100
batch_size = 128

def fit_model(model, optimizer):
    ## TODO: Compile the model
    ## 'rmsprop'
    model.compile(optimizer=optimizer, loss='mean_squared_error')

    ## TODO: Train the model
    hist = model.fit(X_train, y_train,  validation_split = 0.2,
              epochs=epochs, batch_size=batch_size, verbose=1)

    ## TODO: Save the model as model.h5
    ##model.save('my_model.h5')
    
    return hist
In [194]:
for key, value in models.items():
    hist = fit_model(value[0], value[2])
    models[key] = (value[0], hist, value[2], value[3])
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 2s - loss: 0.1130 - val_loss: 0.0610
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0365 - val_loss: 0.0178
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0146 - val_loss: 0.0120
Epoch 4/100
1712/1712 [==============================] - 1s - loss: 0.0119 - val_loss: 0.0112
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0109
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0106
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0109 - val_loss: 0.0104
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0107 - val_loss: 0.0102
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0101
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0099
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0097
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0095
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0094
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0092
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0091
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0089
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0091 - val_loss: 0.0088
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0087
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0086
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0084
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0086 - val_loss: 0.0083
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0082
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0084 - val_loss: 0.0081
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0080
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0082 - val_loss: 0.0079
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0081 - val_loss: 0.0078
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0077
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0078 - val_loss: 0.0076
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0078 - val_loss: 0.0075
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0074
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0074
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0073
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0074 - val_loss: 0.0072
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0071
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0070
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0070
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0069
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0068
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0067
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0067
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0066
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0065
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0066 - val_loss: 0.0065
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0064
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0064
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0063
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0062
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0063 - val_loss: 0.0062
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0061
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0061
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0060
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0060
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0059
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0059
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0059
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0058
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0058
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0057
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0057
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0056
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0056
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0056
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0055
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0055 - val_loss: 0.0055
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0055 - val_loss: 0.0055
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0054
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0054
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0054
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0053
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0053
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0053
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0052
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0052
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0052
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0052
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0051
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0051
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0051
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0051
Epoch 80/100
1712/1712 [==============================] - 1s - loss: 0.0050 - val_loss: 0.0050
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0050
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0050
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0050
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0050
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0049
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0049
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0049
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0049
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0049
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0049
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0048
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0048
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0048ss: 0.004
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0048
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0048
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0048
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0048
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0048
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0047
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0047
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 0.1386 - val_loss: 0.0994
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0869 - val_loss: 0.0531
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0545 - val_loss: 0.0275
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0404 - val_loss: 0.0179
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0347 - val_loss: 0.0144
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0320 - val_loss: 0.0128
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0298 - val_loss: 0.0121
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0288 - val_loss: 0.0116
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0278 - val_loss: 0.0111
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0268 - val_loss: 0.0110
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0261 - val_loss: 0.0107
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0254 - val_loss: 0.0107
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0245 - val_loss: 0.0104
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0238 - val_loss: 0.0101
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0237 - val_loss: 0.0102
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0231 - val_loss: 0.0098
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0226 - val_loss: 0.0097
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0221 - val_loss: 0.0095
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0219 - val_loss: 0.0094
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0211 - val_loss: 0.0094
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0212 - val_loss: 0.0092
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0206 - val_loss: 0.0091
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0204 - val_loss: 0.0090
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0201 - val_loss: 0.0087
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0198 - val_loss: 0.0087
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0195 - val_loss: 0.0086
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0195 - val_loss: 0.0084
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0189 - val_loss: 0.0085
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0185 - val_loss: 0.0085
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0184 - val_loss: 0.0082
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0182 - val_loss: 0.0082
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0181 - val_loss: 0.0083
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0179 - val_loss: 0.0080
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0175 - val_loss: 0.0079
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0173 - val_loss: 0.0078
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0172 - val_loss: 0.0078
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0172 - val_loss: 0.0077
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0170 - val_loss: 0.0077
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0166 - val_loss: 0.0075
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0164 - val_loss: 0.0076
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0164 - val_loss: 0.0075
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0159 - val_loss: 0.0073
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0160 - val_loss: 0.0073
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0159 - val_loss: 0.0072
Epoch 45/100
 896/1712 [==============>...............] - ETA: 1s - loss: 0.0152
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.422610). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.689830). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.212303). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 1s - loss: 0.0155 - val_loss: 0.0072
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0155 - val_loss: 0.0072
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0154 - val_loss: 0.0071
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0151 - val_loss: 0.0070
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0153 - val_loss: 0.0069
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0148 - val_loss: 0.0070
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0147 - val_loss: 0.0069
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0147 - val_loss: 0.0067
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0145 - val_loss: 0.0068
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0143 - val_loss: 0.0067
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0144 - val_loss: 0.0067
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0142 - val_loss: 0.0067
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0142 - val_loss: 0.0066
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0141 - val_loss: 0.0065
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0140 - val_loss: 0.0065
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0139 - val_loss: 0.0064
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0136 - val_loss: 0.0064
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0136 - val_loss: 0.0063
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0134 - val_loss: 0.0063
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0135 - val_loss: 0.0063
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0134 - val_loss: 0.0062
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0133 - val_loss: 0.0063
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0130 - val_loss: 0.0062
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0061
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0129 - val_loss: 0.0060
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0061
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0060
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0126 - val_loss: 0.0060
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0126 - val_loss: 0.0060
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0125 - val_loss: 0.0059
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0125 - val_loss: 0.0058
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0124 - val_loss: 0.0058
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0120 - val_loss: 0.0058
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0122 - val_loss: 0.0058
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0120 - val_loss: 0.0058
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0121 - val_loss: 0.0057
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0119 - val_loss: 0.0057
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0120 - val_loss: 0.0057
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0119 - val_loss: 0.0056
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0116 - val_loss: 0.0055
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0116 - val_loss: 0.0055
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0116 - val_loss: 0.0055
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0115 - val_loss: 0.0055
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0055
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0054
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0054
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0112 - val_loss: 0.0054
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0054
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0112 - val_loss: 0.0054
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0112 - val_loss: 0.0054
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0053
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0053
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0052
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0052
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0052
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0109 - val_loss: 0.0052
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 2s - loss: 0.4249 - val_loss: 0.1410
Epoch 2/100
1712/1712 [==============================] - 2s - loss: 0.2008 - val_loss: 0.1250
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.1450 - val_loss: 0.1098
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.1181 - val_loss: 0.0955
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.1005 - val_loss: 0.0833
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0867 - val_loss: 0.0724
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0784 - val_loss: 0.0633
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0723 - val_loss: 0.0568
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0662 - val_loss: 0.0506
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0614 - val_loss: 0.0460
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0579 - val_loss: 0.0428
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0547 - val_loss: 0.0394
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0519 - val_loss: 0.0366
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0491 - val_loss: 0.0342
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0466 - val_loss: 0.0323
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0448 - val_loss: 0.0307
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0432 - val_loss: 0.0292
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0425 - val_loss: 0.0275
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0404 - val_loss: 0.0259
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0391 - val_loss: 0.0240
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0378 - val_loss: 0.0223
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0369 - val_loss: 0.0213
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0360 - val_loss: 0.0199
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0350 - val_loss: 0.0186
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0344 - val_loss: 0.0172
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0333 - val_loss: 0.0161
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0326 - val_loss: 0.0155
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0323 - val_loss: 0.0145
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0313 - val_loss: 0.0137
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0308 - val_loss: 0.0130
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0299 - val_loss: 0.0123
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0296 - val_loss: 0.0120
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0285 - val_loss: 0.0115
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0285 - val_loss: 0.0110
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0278 - val_loss: 0.0107
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0275 - val_loss: 0.0105
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0268 - val_loss: 0.0103
Epoch 38/100
1712/1712 [==============================] - 2s - loss: 0.0265 - val_loss: 0.0103
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0262 - val_loss: 0.0103
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0255 - val_loss: 0.0102
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0253 - val_loss: 0.0102
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0251 - val_loss: 0.0101
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0245 - val_loss: 0.0105
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0245 - val_loss: 0.0103
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0238 - val_loss: 0.0102
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0237 - val_loss: 0.0102
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0232 - val_loss: 0.0102
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0232 - val_loss: 0.0101
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0228 - val_loss: 0.0104
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0229 - val_loss: 0.0100
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0222 - val_loss: 0.0101
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0220 - val_loss: 0.0099
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0217 - val_loss: 0.0100
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0215 - val_loss: 0.0095
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0214 - val_loss: 0.0098
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0210 - val_loss: 0.0096
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0207 - val_loss: 0.0096
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0207 - val_loss: 0.0092
Epoch 59/100
 384/1712 [=====>........................] - ETA: 4s - loss: 0.0203
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.276626). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.607543). Check your callbacks.
  % delta_t_median)
 896/1712 [==============>...............] - ETA: 1s - loss: 0.0204
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.199447). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.122268). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0202 - val_loss: 0.0094
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0202 - val_loss: 0.0092
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0200 - val_loss: 0.0091ss: 0.
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0198 - val_loss: 0.0092
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0195 - val_loss: 0.0090
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0193 - val_loss: 0.0092
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0192 - val_loss: 0.0089
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0188 - val_loss: 0.0088
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0190 - val_loss: 0.0091
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0186 - val_loss: 0.0089
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0185 - val_loss: 0.0087
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0184 - val_loss: 0.0087
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0180 - val_loss: 0.0086
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0180 - val_loss: 0.0086
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0178 - val_loss: 0.0085
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0179 - val_loss: 0.0082
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0178 - val_loss: 0.0084
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0177 - val_loss: 0.0085
Epoch 77/100
1712/1712 [==============================] - 1s - loss: 0.0174 - val_loss: 0.0085
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0171 - val_loss: 0.0082
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0171 - val_loss: 0.0082
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0176 - val_loss: 0.0080
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0170 - val_loss: 0.0080
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0170 - val_loss: 0.0079
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0166 - val_loss: 0.0080
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0165 - val_loss: 0.0083
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0162 - val_loss: 0.0082
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0164 - val_loss: 0.0082
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0162 - val_loss: 0.0080
Epoch 88/100
1712/1712 [==============================] - 2s - loss: 0.0164 - val_loss: 0.0079
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0166 - val_loss: 0.0078
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0159 - val_loss: 0.0079
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0158 - val_loss: 0.0077
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0158 - val_loss: 0.0077
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0157 - val_loss: 0.0076
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0156 - val_loss: 0.0076
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0156 - val_loss: 0.0075
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0155 - val_loss: 0.0075
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0151 - val_loss: 0.0076
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0152 - val_loss: 0.0074
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0153 - val_loss: 0.0073
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0151 - val_loss: 0.0074
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 1.9066 - val_loss: 0.0191
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0221 - val_loss: 0.0213
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0237 - val_loss: 0.0316
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0190 - val_loss: 0.0330
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0156 - val_loss: 0.0254
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0155 - val_loss: 0.0125
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0921 - val_loss: 0.0096
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0086
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0145
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0144
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0127 - val_loss: 0.0183
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0117 - val_loss: 0.0481
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0063
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0630
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0147 - val_loss: 0.0047
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0080
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0341
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0034
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0139
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0033
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0264
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0055 - val_loss: 0.0034
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0053
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0080 - val_loss: 0.0033
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0039
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0033
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0032
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0053
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0027
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0030
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0026
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0031
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0029
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0040
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0021
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0035
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0043
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0066 - val_loss: 0.0035
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0028
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0046
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0020
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0040
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0025
Epoch 44/100
1712/1712 [==============================] - 1s - loss: 0.0036 - val_loss: 0.0138
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0026
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0024
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0021
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0026
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0019
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0032
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0040
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0017
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0032
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0015
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0071
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0017
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0030
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0026
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0019
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0023
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0016
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0035
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0077
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0015
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0021
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0022
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0015
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 9.3460e-04 - val_loss: 0.0021
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0057
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0017
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0035
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 8.9523e-04 - val_loss: 0.0019
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0025
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 8.0002e-04 - val_loss: 0.0017
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0014
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 5.8246e-04 - val_loss: 0.0016
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0025
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 9.9243e-04 - val_loss: 0.0018
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0016
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 6.2734e-04 - val_loss: 0.0016
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0032
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 8.4891e-04 - val_loss: 0.0015
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0018
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 6.2783e-04 - val_loss: 0.0016
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0014
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 7.1946e-04 - val_loss: 0.0021
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0020
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 7.6738e-04 - val_loss: 0.0018
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0013
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 6.0281e-04 - val_loss: 0.0017
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0021
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 7.1931e-04 - val_loss: 0.0019
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0013
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 5.3944e-04 - val_loss: 0.0020
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0031
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 7.6447e-04 - val_loss: 0.0015
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0014
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 5.0451e-04 - val_loss: 0.0017
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 0.5717 - val_loss: 0.0100
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0225 - val_loss: 0.0074
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0191 - val_loss: 0.0075
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0200 - val_loss: 0.0062
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0194 - val_loss: 0.0076
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0174 - val_loss: 0.0079
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0148 - val_loss: 0.0050
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0138 - val_loss: 0.0048
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0060
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0055
Epoch 11/100
1712/1712 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0041
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0052
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0044
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0040
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0032
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0031
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0027
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0030
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0025
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0023
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0024
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0027
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0018
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0017
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0015
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0019
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0015
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0015
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0013
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0014
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0013
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0014
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0026
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0016
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0014
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0016
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0013
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0016
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0019
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0019
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0013
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0013
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 9.6424e-04 - val_loss: 0.0016
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 9.8378e-04 - val_loss: 0.0013
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 9.4378e-04 - val_loss: 0.0013
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 9.6458e-04 - val_loss: 0.0014
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 9.9407e-04 - val_loss: 0.0021
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 9.7565e-04 - val_loss: 0.0018
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 9.9543e-04 - val_loss: 0.0015
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 9.2377e-04 - val_loss: 0.0018
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 9.1827e-04 - val_loss: 0.0015
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 9.9611e-04 - val_loss: 0.0013
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 8.7406e-04 - val_loss: 0.0014
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 9.7452e-04 - val_loss: 0.0015
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 9.5437e-04 - val_loss: 0.0012
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 8.8950e-04 - val_loss: 0.0015
Epoch 74/100
1712/1712 [==============================] - 1s - loss: 8.4975e-04 - val_loss: 0.0017
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 8.6967e-04 - val_loss: 0.0016
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 9.9745e-04 - val_loss: 0.0014
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 7.8074e-04 - val_loss: 0.0018
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 8.5923e-04 - val_loss: 0.0019
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 8.9878e-04 - val_loss: 0.0018
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 7.3789e-04 - val_loss: 0.0018
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 9.1743e-04 - val_loss: 0.0014
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 8.3754e-04 - val_loss: 0.0025
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 8.4039e-04 - val_loss: 0.0014
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 8.7728e-04 - val_loss: 0.0014
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 7.9957e-04 - val_loss: 0.0013
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 8.1635e-04 - val_loss: 0.0014
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 6.8397e-04 - val_loss: 0.0018
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 8.8725e-04 - val_loss: 0.0018
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 8.4808e-04 - val_loss: 0.0014
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 7.7820e-04 - val_loss: 0.0013
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 8.2833e-04 - val_loss: 0.0013
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 8.4245e-04 - val_loss: 0.0014
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 7.8834e-04 - val_loss: 0.0018
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 7.1078e-04 - val_loss: 0.0015
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 8.0788e-04 - val_loss: 0.0017
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 7.9110e-04 - val_loss: 0.0013
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 7.9715e-04 - val_loss: 0.0016
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 7.4063e-04 - val_loss: 0.0018
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 7.2716e-04 - val_loss: 0.0015
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 7.8370e-04 - val_loss: 0.0015
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 3s - loss: 3.8214 - val_loss: 0.1169
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0379 - val_loss: 0.0743
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0287 - val_loss: 0.0748
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0269 - val_loss: 0.0758
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0276 - val_loss: 0.0546
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0287 - val_loss: 0.0535
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0256 - val_loss: 0.0415
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0216 - val_loss: 0.0167ss: 0
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0237 - val_loss: 0.0100
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0174 - val_loss: 0.0270
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0185 - val_loss: 0.0200
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0131 - val_loss: 0.0080
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0153 - val_loss: 0.0183
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0094
Epoch 15/100
1712/1712 [==============================] - 2s - loss: 0.0133 - val_loss: 0.0192
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0115
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0113ss
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0095
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0084 - val_loss: 0.0090
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0235
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0088 - val_loss: 0.0190
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0071
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0083
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0072
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0078
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0066
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0093
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0104
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0053
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0064
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0084
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0062
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0127
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0075
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0069
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0082
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0136
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0082
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0049
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0082
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0112
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0042
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0047
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0077
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0074
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0087
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0045
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0059
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0060
Epoch 50/100
 896/1712 [==============>...............] - ETA: 1s - loss: 0.0041
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.210100). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0039 - val_loss: 0.0042
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0039
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0041
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0061
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0054
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0036
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0034
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0039ss: 0.
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0037
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0032
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0036
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0033
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0062
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0031
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0033
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0035
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0058
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0033
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0027
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0028
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0029
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0035
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0025
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0029
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0051
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0029
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0026
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0023
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0026
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0025
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0041
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0027
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0042
Epoch 84/100
1712/1712 [==============================] - 2s - loss: 0.0027 - val_loss: 0.0023
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0038
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0028
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0033
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0035
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0025
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0028
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0028
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0030
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0022
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0022
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0031ss:
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0021
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0041
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0028
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0025
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0024
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 0.1207 - val_loss: 0.0139
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0122 - val_loss: 0.0088
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0056
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0043
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0038
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0032
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0027
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0029
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0023
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0022
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0023
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0020
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0019
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0018
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0020
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0019
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0017
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0017
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0017
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0018
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0017
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0016
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0015
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 9.7924e-04 - val_loss: 0.0014
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 9.4547e-04 - val_loss: 0.0015
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 9.4991e-04 - val_loss: 0.0015
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 9.0892e-04 - val_loss: 0.0014
Epoch 33/100
1712/1712 [==============================] - 1s - loss: 8.4744e-04 - val_loss: 0.0016
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 9.0199e-04 - val_loss: 0.0015
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 8.4945e-04 - val_loss: 0.0015
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 7.9532e-04 - val_loss: 0.0014
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 7.6811e-04 - val_loss: 0.0014
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 7.5125e-04 - val_loss: 0.0014
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 7.2743e-04 - val_loss: 0.0014
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 7.1496e-04 - val_loss: 0.0014
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 6.9891e-04 - val_loss: 0.0014
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 7.1716e-04 - val_loss: 0.0014
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 7.1689e-04 - val_loss: 0.0014
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 6.8222e-04 - val_loss: 0.0014
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 7.6594e-04 - val_loss: 0.0014
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 6.8000e-04 - val_loss: 0.0013
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 6.8610e-04 - val_loss: 0.0014
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 6.3370e-04 - val_loss: 0.0014
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 6.2625e-04 - val_loss: 0.0014
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 5.8364e-04 - val_loss: 0.0013
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 5.5286e-04 - val_loss: 0.0013
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 5.3399e-04 - val_loss: 0.0013
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 5.2891e-04 - val_loss: 0.0013
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 5.1867e-04 - val_loss: 0.0013
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 5.2949e-04 - val_loss: 0.0014
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 5.7012e-04 - val_loss: 0.0014
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 5.2280e-04 - val_loss: 0.0013
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 5.0861e-04 - val_loss: 0.0013
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 4.6500e-04 - val_loss: 0.0013
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 4.6659e-04 - val_loss: 0.0013
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 4.4443e-04 - val_loss: 0.0013
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 4.3239e-04 - val_loss: 0.0013
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 4.3773e-04 - val_loss: 0.0013
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 4.7634e-04 - val_loss: 0.0013
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 4.1850e-04 - val_loss: 0.0013
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 3.9266e-04 - val_loss: 0.0013
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 3.9026e-04 - val_loss: 0.0013
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 3.7713e-04 - val_loss: 0.0013
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 3.7910e-04 - val_loss: 0.0013
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 3.7318e-04 - val_loss: 0.0013
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 3.6240e-04 - val_loss: 0.0013
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 3.6240e-04 - val_loss: 0.0013
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 3.4438e-04 - val_loss: 0.0013
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 3.5002e-04 - val_loss: 0.0013
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 3.8773e-04 - val_loss: 0.0014
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 3.6772e-04 - val_loss: 0.0014
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 3.6213e-04 - val_loss: 0.0013
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 3.4513e-04 - val_loss: 0.0013
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 3.1702e-04 - val_loss: 0.0013
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 3.0074e-04 - val_loss: 0.0013
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 3.3000e-04 - val_loss: 0.0013
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 3.1147e-04 - val_loss: 0.0013
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 2.8822e-04 - val_loss: 0.0014
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 3.3028e-04 - val_loss: 0.0013
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 3.1770e-04 - val_loss: 0.0013
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 2.7366e-04 - val_loss: 0.0013
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 2.5533e-04 - val_loss: 0.0013
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 2.6573e-04 - val_loss: 0.0013
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 2.8851e-04 - val_loss: 0.0013
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 2.7346e-04 - val_loss: 0.0013
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 2.6512e-04 - val_loss: 0.0013
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 2.5746e-04 - val_loss: 0.0014
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 2.4418e-04 - val_loss: 0.0013
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 2.3146e-04 - val_loss: 0.0013
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 2.3491e-04 - val_loss: 0.0013
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 2.3743e-04 - val_loss: 0.0013
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 2.2261e-04 - val_loss: 0.0013
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 2.1940e-04 - val_loss: 0.0013
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 2.0733e-04 - val_loss: 0.0013
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 2.2111e-04 - val_loss: 0.0013
Train on 1712 samples, validate on 428 samples
Epoch 1/100
 896/1712 [==============>...............] - ETA: 2s - loss: 0.0783
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.442185). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0572 - val_loss: 0.0143
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0205 - val_loss: 0.0108
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0139 - val_loss: 0.0058
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0051
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0086 - val_loss: 0.0044
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0042
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0042
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0038
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0039
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0039
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0033
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0031
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0028
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0027
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0024
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0023
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0021
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0021
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0022
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0020
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0018
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0022
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0022
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0017
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0017
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0017
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0016
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0016
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0015
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0016
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0015
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0014
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0015
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0014
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0014
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0014
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0014
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0016
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0013
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0014
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0013
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0013
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0012
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0012
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0013
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0013
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0011
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0011
Epoch 60/100
1152/1712 [===================>..........] - ETA: 0s - loss: 0.0012
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.279380). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 1s - loss: 0.0012 - val_loss: 0.0012
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0011
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0012
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0011
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0011
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 9.7898e-04 - val_loss: 0.0012
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 9.7433e-04 - val_loss: 0.0012
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 9.6767e-04 - val_loss: 0.0011
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 9.3088e-04 - val_loss: 0.0011
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 9.2814e-04 - val_loss: 0.0011
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 9.0612e-04 - val_loss: 0.0011
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 9.2345e-04 - val_loss: 0.0011
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 9.1371e-04 - val_loss: 0.0011
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 9.1041e-04 - val_loss: 0.0012
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 9.1089e-04 - val_loss: 0.0011
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 8.8806e-04 - val_loss: 0.0011
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 8.8812e-04 - val_loss: 0.0011
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 8.8490e-04 - val_loss: 0.0011
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 8.5680e-04 - val_loss: 0.0012
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 8.5891e-04 - val_loss: 0.0011
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 8.5991e-04 - val_loss: 0.0011
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 8.3693e-04 - val_loss: 0.0012
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 8.7213e-04 - val_loss: 0.0011
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 8.4826e-04 - val_loss: 0.0011
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 8.4138e-04 - val_loss: 0.0011
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 8.6264e-04 - val_loss: 0.0011
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 8.4457e-04 - val_loss: 0.0012
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 8.2486e-04 - val_loss: 0.0011
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 8.2974e-04 - val_loss: 0.0011
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 8.0405e-04 - val_loss: 0.0011
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 8.0108e-04 - val_loss: 0.0011
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 7.9400e-04 - val_loss: 0.0011
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 2s - loss: 0.2729 - val_loss: 0.1126
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0260 - val_loss: 0.0648
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0167 - val_loss: 0.0358
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0269
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0118 - val_loss: 0.0170
Epoch 6/100
1712/1712 [==============================] - 2s - loss: 0.0108 - val_loss: 0.0160
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0115
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0121
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0166
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0099
Epoch 11/100
1712/1712 [==============================] - 2s - loss: 0.0084 - val_loss: 0.0053
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0061
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0068
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0068
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0047
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0046
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0050
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0069
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0076
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0049
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0049
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0046
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0063 - val_loss: 0.0057
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0047
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0047
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0048
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0046
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0049
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0049ss: 0.0
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0042
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0045
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0041ss
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0047
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0039
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0040
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0036
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0035
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0034
Epoch 39/100
1712/1712 [==============================] - 2s - loss: 0.0040 - val_loss: 0.0032
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0033
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0030
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0030
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0027
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0027
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0028
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0025
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0026
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0025
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0024ss: 0.003 - ETA: 0s - loss: 
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0027
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0026
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0024
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0024
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0022
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0022
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0023
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0022
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0020
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0021
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0020
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0019
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0019
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 74/100
 512/1712 [=======>......................] - ETA: 1s - loss: 0.0025
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.201633). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.101316). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0019
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0021
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0020ss
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0018
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0018
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0018
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0018
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0019
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0019
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0017
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0019
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0020
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0019
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0019
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0016
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0016
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0021
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0021
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0017
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0016
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017ss:
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0018
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 1820.8702 - val_loss: 1.3189
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.7129 - val_loss: 0.4049
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.2855 - val_loss: 0.2268
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.1797 - val_loss: 0.1612
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.1304 - val_loss: 0.1100
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0888 - val_loss: 0.0816
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0684 - val_loss: 0.0639
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0527 - val_loss: 0.0545
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0449 - val_loss: 0.0430
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0369 - val_loss: 0.0372
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0327 - val_loss: 0.0345
Epoch 12/100
1712/1712 [==============================] - 1s - loss: 0.0293 - val_loss: 0.0301
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0274 - val_loss: 0.0279
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0256 - val_loss: 0.0275
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0244 - val_loss: 0.0247
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0231 - val_loss: 0.0233
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0221 - val_loss: 0.0225
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0214 - val_loss: 0.0216
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0204 - val_loss: 0.0206
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0196 - val_loss: 0.0197
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0191 - val_loss: 0.0190
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0185 - val_loss: 0.0183
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0175 - val_loss: 0.0177
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0169 - val_loss: 0.0172
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0163 - val_loss: 0.0164
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0157 - val_loss: 0.0160
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0152 - val_loss: 0.0154
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0148 - val_loss: 0.0149
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0143 - val_loss: 0.0145
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0139 - val_loss: 0.0147
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0135 - val_loss: 0.0138
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0132 - val_loss: 0.0134
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0130 - val_loss: 0.0135
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0126 - val_loss: 0.0128
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0124 - val_loss: 0.0125
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0121 - val_loss: 0.0122
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0119 - val_loss: 0.0120
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0117 - val_loss: 0.0120
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0115 - val_loss: 0.0118
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0114
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0112
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0109 - val_loss: 0.0110
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0107 - val_loss: 0.0116
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0107
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0109
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0104
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0102
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0101
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0102
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0099
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0099
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0097
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0096
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0098
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0093
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0091 - val_loss: 0.0095
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0091
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0092
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0090
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0088 - val_loss: 0.0089
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0089
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0087
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0086
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0086
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0088
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0088
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0085
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0081 - val_loss: 0.0083
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0081 - val_loss: 0.0082
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0080 - val_loss: 0.0083
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0080 - val_loss: 0.0081
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0081
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0080 - val_loss: 0.0083
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0080
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0081
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0079
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0078
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0077
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0078
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0076
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0074 - val_loss: 0.0077
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0074 - val_loss: 0.0076
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0075
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0074
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0074
Epoch 86/100
1712/1712 [==============================] - 1s - loss: 0.0071 - val_loss: 0.0074
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0074
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0074
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0072
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0072
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0072
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0072
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0072
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0073
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0070
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0071
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0070
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0071
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0072
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0068
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 80.1747 - val_loss: 0.0112
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0160 - val_loss: 0.0079
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0062
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0064
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0087
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0067
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0084 - val_loss: 0.0052
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0079 - val_loss: 0.0046
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0046
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0044
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0046
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0045
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0058
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0066 - val_loss: 0.0046
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0063 - val_loss: 0.0062
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0041
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0041
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0040
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0040
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0045
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0039
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0043
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0038
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0047
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0040
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0037
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0038
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0035
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0035
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0036
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0037
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0033
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0036
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0035
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0036
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0032
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0038
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0030
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0045
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0030
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0035
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0028
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0030
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0027
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0029
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0027
Epoch 47/100
 896/1712 [==============>...............] - ETA: 1s - loss: 0.0037
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.878767). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.716001). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.553235). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.277118). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 1s - loss: 0.0037 - val_loss: 0.0026
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0028
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0026
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0027
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0031
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0025
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0024
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0025
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0025
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0025
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0025
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0027
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0024
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0025
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0023
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0024
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0024
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0028
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0023
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0021
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0023
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0021
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0021
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0021
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0020
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0022
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0020
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0020
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0020
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0020
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0019
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0020
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0019
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0022
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0019
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0019
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s - loss: 445.0360 - val_loss: 0.0724
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0405 - val_loss: 0.0572
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0288 - val_loss: 0.0488
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0253 - val_loss: 0.0295
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0211 - val_loss: 0.0284
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0198 - val_loss: 0.0223
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0183 - val_loss: 0.0123
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0165 - val_loss: 0.0104
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0159 - val_loss: 0.0067
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0162 - val_loss: 0.0089
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0149 - val_loss: 0.0123
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0143 - val_loss: 0.0083
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0133 - val_loss: 0.0100
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0138 - val_loss: 0.0111ss: 0.
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0133 - val_loss: 0.0095
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0130 - val_loss: 0.0074
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0129 - val_loss: 0.0106
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0123 - val_loss: 0.0076
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0128 - val_loss: 0.0046
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0123 - val_loss: 0.0080
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0114 - val_loss: 0.0081
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0120 - val_loss: 0.0105
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0117 - val_loss: 0.0095
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0123 - val_loss: 0.0067
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0095
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0112 - val_loss: 0.0044
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0070
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0048
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0046
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0108 - val_loss: 0.0056
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0107 - val_loss: 0.0058
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0111 - val_loss: 0.0046
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0050
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0061
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0054
Epoch 36/100
1712/1712 [==============================] - 2s - loss: 0.0109 - val_loss: 0.0060
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0053
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0102 - val_loss: 0.0058
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0103 - val_loss: 0.0058
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0048
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0045
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0055
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0056
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0104 - val_loss: 0.0081
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0046
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0048
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0047
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0069
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0063
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0055
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0064
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0053
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0052
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0101 - val_loss: 0.0056
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0046
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0046
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0100 - val_loss: 0.0050
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0055
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0072
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0119
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0054
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0047
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0099 - val_loss: 0.0050
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0049
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0061
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0073
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0063
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0071
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0091 - val_loss: 0.0053
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0046
Epoch 71/100
 768/1712 [============>.................] - ETA: 2s - loss: 0.0096
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.271081). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.541162). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0095 - val_loss: 0.0096
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0096 - val_loss: 0.0074
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0046
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0088 - val_loss: 0.0046
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0064
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0075
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0052
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0076
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0046
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0052
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0053
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0088 - val_loss: 0.0052
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0063
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0091 - val_loss: 0.0045
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0051
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0049
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0074
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0049
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0087 - val_loss: 0.0056
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0086 - val_loss: 0.0050
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0047
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0094 - val_loss: 0.0054
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0048
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0088 - val_loss: 0.0049
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0048
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0066
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0086 - val_loss: 0.0059
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0057ss: 0.
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0060
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0055
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 1s - loss: 1.1589 - val_loss: 0.0261
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0228 - val_loss: 0.0113
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0108 - val_loss: 0.0110
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0124 - val_loss: 0.0186
Epoch 5/100
1712/1712 [==============================] - 1s - loss: 0.0117 - val_loss: 0.0068
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0067
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0056
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0092
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0048
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0051
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0057
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0326 - val_loss: 1.0927
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.2320 - val_loss: 0.0241
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0138 - val_loss: 0.0078
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0065
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0115
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0050
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0047
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0058
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0057 - val_loss: 0.0060
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0052
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0040
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0042
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0081
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0039
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0046
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0058
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0043
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0041
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0039
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0050
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0039
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0035
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0040
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0033
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0033
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0054
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0031
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0032
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0034
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0031
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0035
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0046
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0029
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0031
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0037
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0034
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0030
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0028
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0029
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0036
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0028
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0030
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0027
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0034
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0025
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0029
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0050
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0031
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0026
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0029
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0027
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0042
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0026
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0026
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0033
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0029
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0024
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0027
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0026
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0023
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0030
Epoch 73/100
1280/1712 [=====================>........] - ETA: 0s - loss: 0.0026
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.148787). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.118130). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 1s - loss: 0.0024 - val_loss: 0.0023
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0022
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0028
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0028
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0024
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0024
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0036
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0028
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0022
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0023
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0024
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0022
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0021
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0022
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0025
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0023
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0022
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0020
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0021
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0025
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0023
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0021
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0020
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0045
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0021
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0020
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0019
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0019
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 2s - loss: 0.5126 - val_loss: 0.0106
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0191 - val_loss: 0.0085
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0133 - val_loss: 0.0075
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0117 - val_loss: 0.0064
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0068
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0084 - val_loss: 0.0063
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0042
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0043
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0048
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0051
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0038
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0034
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0035
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0031
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0034
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0028
Epoch 17/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0044
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0026
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0030
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0035
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0027
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0023
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0023
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0023
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0021
Epoch 28/100
1712/1712 [==============================] - 1s - loss: 0.0029 - val_loss: 0.0027
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0020
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0024
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0021
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0024
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0023
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0021
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0019
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0019
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 40/100
1712/1712 [==============================] - 1s - loss: 0.0023 - val_loss: 0.0018
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0024
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0018
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0020
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0018
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0018
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0020
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0020
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 50/100
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0017
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0026
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0018
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0018
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0015
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0016
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0023
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0015
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0018
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0015
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0018
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0014
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0019
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0017
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 81/100
1712/1712 [==============================] - 1s - loss: 0.0013 - val_loss: 0.0014
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 83/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0016
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0017
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0017
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 2s - loss: 2.3315 - val_loss: 0.1146
Epoch 2/100
1712/1712 [==============================] - 0s - loss: 0.0307 - val_loss: 0.0791
Epoch 3/100
1712/1712 [==============================] - 0s - loss: 0.0206 - val_loss: 0.0520
Epoch 4/100
1712/1712 [==============================] - 0s - loss: 0.0157 - val_loss: 0.0238
Epoch 5/100
1712/1712 [==============================] - 0s - loss: 0.0158 - val_loss: 0.0136
Epoch 6/100
1712/1712 [==============================] - 0s - loss: 0.0139 - val_loss: 0.0259
Epoch 7/100
1712/1712 [==============================] - 0s - loss: 0.0132 - val_loss: 0.0219
Epoch 8/100
1712/1712 [==============================] - 0s - loss: 0.0119 - val_loss: 0.0112
Epoch 9/100
1712/1712 [==============================] - 0s - loss: 0.0131 - val_loss: 0.0065
Epoch 10/100
1712/1712 [==============================] - 0s - loss: 0.0127 - val_loss: 0.0048
Epoch 11/100
1712/1712 [==============================] - 0s - loss: 0.0117 - val_loss: 0.0152
Epoch 12/100
1712/1712 [==============================] - 0s - loss: 0.0112 - val_loss: 0.0198
Epoch 13/100
1712/1712 [==============================] - 0s - loss: 0.0118 - val_loss: 0.0259
Epoch 14/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0167
Epoch 15/100
1712/1712 [==============================] - 0s - loss: 0.0108 - val_loss: 0.0079
Epoch 16/100
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0070
Epoch 17/100
1712/1712 [==============================] - 2s - loss: 0.0082 - val_loss: 0.0080
Epoch 18/100
1712/1712 [==============================] - 0s - loss: 0.0105 - val_loss: 0.0060
Epoch 19/100
1712/1712 [==============================] - 0s - loss: 0.0081 - val_loss: 0.0048
Epoch 20/100
1712/1712 [==============================] - 0s - loss: 0.0098 - val_loss: 0.0045
Epoch 21/100
1712/1712 [==============================] - 0s - loss: 0.0089 - val_loss: 0.0091
Epoch 22/100
1712/1712 [==============================] - 0s - loss: 0.0097 - val_loss: 0.0048
Epoch 23/100
1712/1712 [==============================] - 0s - loss: 0.0078 - val_loss: 0.0045
Epoch 24/100
1712/1712 [==============================] - 0s - loss: 0.0078 - val_loss: 0.0045
Epoch 25/100
1712/1712 [==============================] - 0s - loss: 0.0095 - val_loss: 0.0049
Epoch 26/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0045
Epoch 27/100
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0061
Epoch 28/100
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0047
Epoch 29/100
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0046
Epoch 30/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0074
Epoch 31/100
1712/1712 [==============================] - 0s - loss: 0.0075 - val_loss: 0.0052
Epoch 32/100
1712/1712 [==============================] - 0s - loss: 0.0069 - val_loss: 0.0081
Epoch 33/100
1712/1712 [==============================] - 0s - loss: 0.0065 - val_loss: 0.0054
Epoch 34/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0095
Epoch 35/100
1712/1712 [==============================] - 0s - loss: 0.0063 - val_loss: 0.0081
Epoch 36/100
1712/1712 [==============================] - 0s - loss: 0.0063 - val_loss: 0.0106
Epoch 37/100
1712/1712 [==============================] - 0s - loss: 0.0067 - val_loss: 0.0048
Epoch 38/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0051
Epoch 39/100
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0074
Epoch 40/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0055
Epoch 41/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0054
Epoch 42/100
1712/1712 [==============================] - 0s - loss: 0.0061 - val_loss: 0.0083
Epoch 43/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0044
Epoch 44/100
1712/1712 [==============================] - 0s - loss: 0.0053 - val_loss: 0.0050
Epoch 45/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0043
Epoch 46/100
1712/1712 [==============================] - 0s - loss: 0.0055 - val_loss: 0.0046
Epoch 47/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0038
Epoch 48/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0039
Epoch 49/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0038
Epoch 50/100
1712/1712 [==============================] - 2s - loss: 0.0048 - val_loss: 0.0038
Epoch 51/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0037
Epoch 52/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0037
Epoch 53/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0035
Epoch 54/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0034
Epoch 55/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0036
Epoch 56/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0037
Epoch 57/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0035
Epoch 58/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0032
Epoch 59/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0031
Epoch 60/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0031
Epoch 61/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0032
Epoch 62/100
1712/1712 [==============================] - 0s - loss: 0.0038 - val_loss: 0.0032
Epoch 63/100
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0035
Epoch 64/100
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0030
Epoch 65/100
1712/1712 [==============================] - 0s - loss: 0.0789 - val_loss: 0.0434
Epoch 66/100
1712/1712 [==============================] - 0s - loss: 0.0110 - val_loss: 0.0348
Epoch 67/100
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0171
Epoch 68/100
1712/1712 [==============================] - 0s - loss: 0.0062 - val_loss: 0.0055
Epoch 69/100
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0051
Epoch 70/100
1712/1712 [==============================] - 0s - loss: 0.0055 - val_loss: 0.0051
Epoch 71/100
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0048
Epoch 72/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0043
Epoch 73/100
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0043
Epoch 74/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0042
Epoch 75/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0042
Epoch 76/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0042
Epoch 77/100
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0042
Epoch 78/100
1712/1712 [==============================] - 0s - loss: 0.0080 - val_loss: 0.0043
Epoch 79/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0043
Epoch 80/100
1712/1712 [==============================] - 0s - loss: 0.0050 - val_loss: 0.0042
Epoch 81/100
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0042
Epoch 82/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0041
Epoch 83/100
1712/1712 [==============================] - 2s - loss: 0.0048 - val_loss: 0.0041
Epoch 84/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0041
Epoch 85/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0042
Epoch 86/100
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0042
Epoch 87/100
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0040
Epoch 88/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0040
Epoch 89/100
1712/1712 [==============================] - 0s - loss: 0.0045 - val_loss: 0.0040
Epoch 90/100
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0037
Epoch 91/100
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0037
Epoch 92/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0036
Epoch 93/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0038
Epoch 94/100
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0036
Epoch 95/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0036
Epoch 96/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0037
Epoch 97/100
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0035
Epoch 98/100
1712/1712 [==============================] - 0s - loss: 0.0041 - val_loss: 0.0036
Epoch 99/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0036
Epoch 100/100
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0034

Step 7: Visualize the Loss and Test Predictions

(IMPLEMENTATION) Answer a few questions and visualize the loss

Question 1: Outline the steps you took to get to your final neural network architecture and your reasoning at each step.

Answer: As always it is a good idea to start with simple architecture to see how it performs. Then try different tricks - like increasing number of convolutional layers and fully connected layers, trying different filter sizes, regularization techniques, batch normalization. I've started with 3 convolutional layers followed by MaxPooling layers and two fully-connected layers. Playing with filter size I determined that 3x3 filter works slightly better than other variations. As it can be seen from adam graph below - this simple model tends to overfit - so I've been enhancing it with additional layers and adding dropout. This allowed to slightly decrease validation loss and reduce overfitting a lot. Also, I was made attempt to see if more complex models can provide some additional benefits - adding more layers and some advanced techniques as Batch Normalization removed overfitting completely but loss was slightly higher than for previous simpler model. Even increasing number of epochs to 300 and give more training time for complex models did not help to outperform previous simpler model. So "Intermediate model" from graphs below was selected as final model for project.

Question 2: Defend your choice of optimizer. Which optimizers did you test, and how did you determine which worked best?

Answer: I've tried three candidate models with different model complexities with different optimizers: simple SGD, adam, adagrad, rmsprop and nadam. The results are clearly visualized below. Simple SDG is failed to converge to minimal in 100 epochs for all models. Adam shown the best results in both terms of convergence speed and stability (graph is smooth - no fluctuations). Although "rmsprop" converges pretty fast to low loss values it has high fluctuations and less stability than adam - especially this can be seen for complex model. Adagrad performed better then SGD but still not good enough compared to adam and rmsprop and unstable for complex model. Nadam is good for intermediate model (which was selected as a final model) - however it has some weird issues for complex model. So, taking into consideration all these facts and graphs - adam is selected as final optimizer as it has both fast convergence and stability for all three models.

Use the code cell below to plot the training and validation loss of your neural network. You may find this resource useful.

In [ ]:
data = [(i, key, value) for i, (key, value) in enumerate(models.items())]

for i, key, value 
In [195]:
## TODO: Visualize the training and validation loss of your neural network 
data = [(i, key, value) for i, (key, value) in enumerate(models.items())]


fig = plt.figure(figsize = (15, 20))
plt.axis('off')
plt.tight_layout()

for i, key, value in data[0:9]:
    if i < 12:
        hist = value[1]
        ax1 = fig.add_subplot(330 + i + 1)
        #plt.setp([ax1], xticks=[], yticks=[])
        ax1.set_ylim(0, 0.02)
        ax1.set_xticks([])
        ax1.set_yticks([])
        ax1.set_title(value[3] + ' - Optimizer: ' + value[2])
        ax1.set_ylabel('loss')
        #ax1.set_xlabel('epoch')
        ax1.plot(hist.history['loss'])
        ax1.plot(hist.history['val_loss'])
        ax1.xaxis.set_ticks(np.arange(0, epochs, epochs // 10))
        ax1.yaxis.set_ticks(np.arange(0, 0.02, 0.002))

        
        
In [202]:
fig = plt.figure(figsize = (15, 20))
plt.axis('off')
plt.tight_layout()

for i, key, value in data[9:]:
    hist = value[1]
    ax1 = fig.add_subplot(230 + i + 1 - 9)
    #plt.setp([ax1], xticks=[], yticks=[])
    ax1.set_ylim(0, 0.02)
    ax1.set_xticks([])
    ax1.set_yticks([])
    ax1.set_title(value[3] + ' - Optimizer: ' + value[2])
    ax1.set_ylabel('loss')
    #ax1.set_xlabel('epoch')
    ax1.plot(hist.history['loss'])
    ax1.plot(hist.history['val_loss'])
    ax1.xaxis.set_ticks(np.arange(0, epochs, epochs // 10))
    ax1.yaxis.set_ticks(np.arange(0, 0.02, 0.002))

  
In [203]:
model = getModel30()
epochs = 300
batch_size = 128

hist = fit_model(model, 'adam')
Train on 1712 samples, validate on 428 samples
Epoch 1/300
1712/1712 [==============================] - 3s - loss: 0.2857 - val_loss: 0.1209
Epoch 2/300
1712/1712 [==============================] - 0s - loss: 0.0294 - val_loss: 0.0699
Epoch 3/300
1712/1712 [==============================] - 0s - loss: 0.0173 - val_loss: 0.0435
Epoch 4/300
1024/1712 [================>.............] - ETA: 1s - loss: 0.0133
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.144343). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.123793). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0129 - val_loss: 0.0323
Epoch 5/300
1712/1712 [==============================] - 0s - loss: 0.0113 - val_loss: 0.0138
Epoch 6/300
1712/1712 [==============================] - 0s - loss: 0.0106 - val_loss: 0.0146
Epoch 7/300
1712/1712 [==============================] - 0s - loss: 0.0092 - val_loss: 0.0115
Epoch 8/300
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0124
Epoch 9/300
1712/1712 [==============================] - 0s - loss: 0.0093 - val_loss: 0.0110
Epoch 10/300
1712/1712 [==============================] - 0s - loss: 0.0090 - val_loss: 0.0054
Epoch 11/300
1712/1712 [==============================] - 0s - loss: 0.0083 - val_loss: 0.0080
Epoch 12/300
1712/1712 [==============================] - 0s - loss: 0.0077 - val_loss: 0.0047
Epoch 13/300
1712/1712 [==============================] - 0s - loss: 0.0076 - val_loss: 0.0054
Epoch 14/300
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0060
Epoch 15/300
1712/1712 [==============================] - 0s - loss: 0.0072 - val_loss: 0.0048
Epoch 16/300
1712/1712 [==============================] - 0s - loss: 0.0070 - val_loss: 0.0051
Epoch 17/300
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0052
Epoch 18/300
1712/1712 [==============================] - 0s - loss: 0.0066 - val_loss: 0.0062
Epoch 19/300
1712/1712 [==============================] - 0s - loss: 0.0073 - val_loss: 0.0059
Epoch 20/300
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0047
Epoch 21/300
1712/1712 [==============================] - 0s - loss: 0.0066 - val_loss: 0.0052
Epoch 22/300
1712/1712 [==============================] - 0s - loss: 0.0071 - val_loss: 0.0060
Epoch 23/300
1712/1712 [==============================] - 0s - loss: 0.0064 - val_loss: 0.0048
Epoch 24/300
1712/1712 [==============================] - 0s - loss: 0.0059 - val_loss: 0.0048
Epoch 25/300
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0048
Epoch 26/300
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0047
Epoch 27/300
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0050
Epoch 28/300
1712/1712 [==============================] - 0s - loss: 0.0058 - val_loss: 0.0050
Epoch 29/300
1712/1712 [==============================] - 0s - loss: 0.0056 - val_loss: 0.0047
Epoch 30/300
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0049
Epoch 31/300
1712/1712 [==============================] - 0s - loss: 0.0051 - val_loss: 0.0045
Epoch 32/300
1712/1712 [==============================] - 0s - loss: 0.0052 - val_loss: 0.0043
Epoch 33/300
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0042
Epoch 34/300
1712/1712 [==============================] - 0s - loss: 0.0048 - val_loss: 0.0041
Epoch 35/300
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0039
Epoch 36/300
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0039
Epoch 37/300
1712/1712 [==============================] - 0s - loss: 0.0047 - val_loss: 0.0037
Epoch 38/300
1712/1712 [==============================] - 2s - loss: 0.0044 - val_loss: 0.0045
Epoch 39/300
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0034
Epoch 40/300
1712/1712 [==============================] - 0s - loss: 0.0044 - val_loss: 0.0033
Epoch 41/300
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0037
Epoch 42/300
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0056
Epoch 43/300
1712/1712 [==============================] - 0s - loss: 0.0043 - val_loss: 0.0042
Epoch 44/300
1712/1712 [==============================] - 0s - loss: 0.0042 - val_loss: 0.0030
Epoch 45/300
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0030
Epoch 46/300
1712/1712 [==============================] - 0s - loss: 0.0037 - val_loss: 0.0028
Epoch 47/300
1712/1712 [==============================] - 0s - loss: 0.0036 - val_loss: 0.0028
Epoch 48/300
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0026
Epoch 49/300
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0026
Epoch 50/300
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0030
Epoch 51/300
1712/1712 [==============================] - 0s - loss: 0.0034 - val_loss: 0.0024
Epoch 52/300
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0024
Epoch 53/300
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0026
Epoch 54/300
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0023
Epoch 55/300
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0023
Epoch 56/300
1712/1712 [==============================] - 0s - loss: 0.0032 - val_loss: 0.0025
Epoch 57/300
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0023
Epoch 58/300
1712/1712 [==============================] - 0s - loss: 0.0031 - val_loss: 0.0022
Epoch 59/300
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0022
Epoch 60/300
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0024
Epoch 61/300
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 62/300
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0022
Epoch 63/300
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0025
Epoch 64/300
1712/1712 [==============================] - 0s - loss: 0.0029 - val_loss: 0.0025
Epoch 65/300
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0021
Epoch 66/300
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0020
Epoch 67/300
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0020
Epoch 68/300
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0019
Epoch 69/300
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0022
Epoch 70/300
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 71/300
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0021
Epoch 72/300
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 73/300
 640/1712 [==========>...................] - ETA: 2s - loss: 0.0027
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.950425). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.642730). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.335034). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.168018). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0026 - val_loss: 0.0020
Epoch 74/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 75/300
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 76/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 77/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 78/300
1712/1712 [==============================] - 0s - loss: 0.0025 - val_loss: 0.0020
Epoch 79/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 80/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0019
Epoch 81/300
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0020
Epoch 82/300
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0018
Epoch 83/300
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0021
Epoch 84/300
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0019
Epoch 85/300
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0019
Epoch 86/300
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0020
Epoch 87/300
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0017
Epoch 88/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0019
Epoch 89/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0020
Epoch 90/300
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0018
Epoch 91/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 92/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0018
Epoch 93/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0017
Epoch 94/300
1712/1712 [==============================] - ETA: 0s - loss: 0.002 - 0s - loss: 0.0021 - val_loss: 0.0017
Epoch 95/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0018
Epoch 96/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0019
Epoch 97/300
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0017
Epoch 98/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 99/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0019
Epoch 100/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0017
Epoch 101/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0019
Epoch 102/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0018
Epoch 103/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 104/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 105/300
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0017
Epoch 106/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0018
Epoch 107/300
1712/1712 [==============================] - 2s - loss: 0.0019 - val_loss: 0.0016
Epoch 108/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0019
Epoch 109/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0017
Epoch 110/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0018
Epoch 111/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 112/300
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0017
Epoch 113/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0018
Epoch 114/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 115/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 116/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 117/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 118/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 119/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 120/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0018
Epoch 121/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0017
Epoch 122/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0017
Epoch 123/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 124/300
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0016
Epoch 125/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 126/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 127/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 128/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 129/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 130/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0017
Epoch 131/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0017
Epoch 132/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0015
Epoch 133/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0017
Epoch 134/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0016
Epoch 135/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0017
Epoch 136/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0017
Epoch 137/300
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0016
Epoch 138/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 139/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 140/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 141/300
1712/1712 [==============================] - 2s - loss: 0.0016 - val_loss: 0.0015
Epoch 142/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 143/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 144/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0017
Epoch 145/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 146/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 147/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 148/300
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0015
Epoch 149/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 150/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0017
Epoch 151/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0018
Epoch 152/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0017
Epoch 153/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 154/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 155/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 156/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 157/300
1712/1712 [==============================] - ETA: 0s - loss: 0.001 - 0s - loss: 0.0015 - val_loss: 0.0014
Epoch 158/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0014
Epoch 159/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0015
Epoch 160/300
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0016
Epoch 161/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 162/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0017
Epoch 163/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0016
Epoch 164/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 165/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 166/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 167/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 168/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 169/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 170/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0018
Epoch 171/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 172/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 173/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 174/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0014
Epoch 175/300
1712/1712 [==============================] - 2s - loss: 0.0013 - val_loss: 0.0015
Epoch 176/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 177/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 178/300
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0015
Epoch 179/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 180/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 181/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0017
Epoch 182/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 183/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 184/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 185/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0017
Epoch 186/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0016
Epoch 187/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 188/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 189/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 190/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0015
Epoch 191/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 192/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 193/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 194/300
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 195/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 196/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 197/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 198/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 199/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 200/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 201/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 202/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 203/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 204/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 205/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 206/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 207/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 208/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 209/300
1712/1712 [==============================] - 2s - loss: 0.0012 - val_loss: 0.0014
Epoch 210/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 211/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 212/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 213/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0014
Epoch 214/300
1712/1712 [==============================] - ETA: 0s - loss: 0.001 - 0s - loss: 0.0011 - val_loss: 0.0016
Epoch 215/300
1712/1712 [==============================] - 2s - loss: 0.0012 - val_loss: 0.0015
Epoch 216/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 217/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 218/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 219/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 220/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0018
Epoch 221/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0016
Epoch 222/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 223/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 224/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 225/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 226/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 227/300
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0015
Epoch 228/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 229/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 230/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 231/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 232/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 233/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 234/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 235/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 236/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 237/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 238/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0015
Epoch 239/300
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0014
Epoch 240/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0013
Epoch 241/300
 896/1712 [==============>...............] - ETA: 1s - loss: 0.0011
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.276529). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 0.0011 - val_loss: 0.0015
Epoch 242/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 243/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0015
Epoch 244/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 245/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 246/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 247/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 248/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 249/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 250/300
1712/1712 [==============================] - 0s - loss: 9.9861e-04 - val_loss: 0.0014
Epoch 251/300
1712/1712 [==============================] - 0s - loss: 9.9916e-04 - val_loss: 0.0015
Epoch 252/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0015
Epoch 253/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 254/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0015
Epoch 255/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 256/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 257/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0013
Epoch 258/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 259/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 260/300
1712/1712 [==============================] - 0s - loss: 9.9079e-04 - val_loss: 0.0015
Epoch 261/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0015
Epoch 262/300
1712/1712 [==============================] - 0s - loss: 9.7298e-04 - val_loss: 0.0014
Epoch 263/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 264/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 265/300
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0014
Epoch 266/300
1712/1712 [==============================] - 0s - loss: 9.7921e-04 - val_loss: 0.0014
Epoch 267/300
1712/1712 [==============================] - 0s - loss: 9.5461e-04 - val_loss: 0.0013
Epoch 268/300
1712/1712 [==============================] - 0s - loss: 9.8940e-04 - val_loss: 0.0015
Epoch 269/300
1712/1712 [==============================] - 0s - loss: 9.8235e-04 - val_loss: 0.0014
Epoch 270/300
1712/1712 [==============================] - 0s - loss: 9.5451e-04 - val_loss: 0.0013
Epoch 271/300
1712/1712 [==============================] - 0s - loss: 9.5263e-04 - val_loss: 0.0014
Epoch 272/300
1712/1712 [==============================] - 0s - loss: 9.5707e-04 - val_loss: 0.0015
Epoch 273/300
1712/1712 [==============================] - 0s - loss: 9.3917e-04 - val_loss: 0.0014
Epoch 274/300
1712/1712 [==============================] - 0s - loss: 9.7084e-04 - val_loss: 0.0014
Epoch 275/300
 768/1712 [============>.................] - ETA: 2s - loss: 8.9057e-04
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.234896). Check your callbacks.
  % delta_t_median)
C:\Users\Alexey\AppData\Local\conda\conda\envs\aind-cv\lib\site-packages\keras\callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.468792). Check your callbacks.
  % delta_t_median)
1712/1712 [==============================] - 2s - loss: 9.1511e-04 - val_loss: 0.0014
Epoch 276/300
1712/1712 [==============================] - 0s - loss: 9.2840e-04 - val_loss: 0.0014
Epoch 277/300
1712/1712 [==============================] - 0s - loss: 9.2785e-04 - val_loss: 0.0014
Epoch 278/300
1712/1712 [==============================] - 0s - loss: 9.3323e-04 - val_loss: 0.0013
Epoch 279/300
1712/1712 [==============================] - 0s - loss: 9.3959e-04 - val_loss: 0.0014
Epoch 280/300
1712/1712 [==============================] - 0s - loss: 9.6515e-04 - val_loss: 0.0014
Epoch 281/300
1712/1712 [==============================] - 0s - loss: 9.4213e-04 - val_loss: 0.0014
Epoch 282/300
1712/1712 [==============================] - 0s - loss: 9.2789e-04 - val_loss: 0.0015
Epoch 283/300
1712/1712 [==============================] - 0s - loss: 9.1680e-04 - val_loss: 0.0014
Epoch 284/300
1712/1712 [==============================] - 0s - loss: 9.2395e-04 - val_loss: 0.0014
Epoch 285/300
1712/1712 [==============================] - 0s - loss: 9.3540e-04 - val_loss: 0.0014
Epoch 286/300
1712/1712 [==============================] - 0s - loss: 9.2901e-04 - val_loss: 0.0013
Epoch 287/300
1712/1712 [==============================] - 0s - loss: 8.9147e-04 - val_loss: 0.0014
Epoch 288/300
1712/1712 [==============================] - 0s - loss: 9.1563e-04 - val_loss: 0.0014
Epoch 289/300
1712/1712 [==============================] - 0s - loss: 9.0337e-04 - val_loss: 0.0014
Epoch 290/300
1712/1712 [==============================] - 0s - loss: 8.9179e-04 - val_loss: 0.0014
Epoch 291/300
1712/1712 [==============================] - 0s - loss: 9.1357e-04 - val_loss: 0.0014
Epoch 292/300
1712/1712 [==============================] - 0s - loss: 9.0871e-04 - val_loss: 0.0013
Epoch 293/300
1712/1712 [==============================] - 0s - loss: 8.8765e-04 - val_loss: 0.0014
Epoch 294/300
1712/1712 [==============================] - 0s - loss: 9.1578e-04 - val_loss: 0.0014
Epoch 295/300
1712/1712 [==============================] - 0s - loss: 8.9558e-04 - val_loss: 0.0013
Epoch 296/300
1712/1712 [==============================] - 0s - loss: 8.5223e-04 - val_loss: 0.0013
Epoch 297/300
1712/1712 [==============================] - 0s - loss: 8.5933e-04 - val_loss: 0.0014
Epoch 298/300
1712/1712 [==============================] - 0s - loss: 8.7348e-04 - val_loss: 0.0014
Epoch 299/300
1712/1712 [==============================] - 0s - loss: 8.6041e-04 - val_loss: 0.0014
Epoch 300/300
1712/1712 [==============================] - 0s - loss: 8.5810e-04 - val_loss: 0.0014
In [212]:
fig = plt.figure(figsize = (10, 10))

ax1 = fig.add_subplot(111)
ax1.set_ylim(0, 0.02)
ax1.set_title('Complex model 300 epochs - optimizer: adam')
ax1.set_ylabel('loss')
ax1.plot(hist.history['loss'])
ax1.plot(hist.history['val_loss'])
Out[212]:
[<matplotlib.lines.Line2D at 0x1938767a550>]
In [23]:
# final model
# slight modiification of Intermidiate model above
def getModel21():
    model = Sequential()
    model.add(Convolution2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='same',
                    input_shape=(96, 96, 1), use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Convolution2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', use_bias=True))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
    model.add(Flatten())
    model.add(Dense(1024, activation="relu"))
    model.add(Dropout(0.35))
    model.add(Dense(512, activation="relu"))
    model.add(Dropout(0.35))
    model.add(Dense(entities_count))

    return model

model = getModel21()
model.summary()
epochs = 75
batch_size = 64
hist = fit_model(model, 'adam')

model.save('my_model.h5')
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_61 (Conv2D)           (None, 96, 96, 16)        160       
_________________________________________________________________
activation_46 (Activation)   (None, 96, 96, 16)        0         
_________________________________________________________________
max_pooling2d_61 (MaxPooling (None, 48, 48, 16)        0         
_________________________________________________________________
conv2d_62 (Conv2D)           (None, 48, 48, 32)        4640      
_________________________________________________________________
activation_47 (Activation)   (None, 48, 48, 32)        0         
_________________________________________________________________
max_pooling2d_62 (MaxPooling (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_63 (Conv2D)           (None, 24, 24, 64)        18496     
_________________________________________________________________
activation_48 (Activation)   (None, 24, 24, 64)        0         
_________________________________________________________________
max_pooling2d_63 (MaxPooling (None, 12, 12, 64)        0         
_________________________________________________________________
flatten_16 (Flatten)         (None, 9216)              0         
_________________________________________________________________
dense_41 (Dense)             (None, 1024)              9438208   
_________________________________________________________________
dropout_21 (Dropout)         (None, 1024)              0         
_________________________________________________________________
dense_42 (Dense)             (None, 512)               524800    
_________________________________________________________________
dropout_22 (Dropout)         (None, 512)               0         
_________________________________________________________________
dense_43 (Dense)             (None, 30)                15390     
=================================================================
Total params: 10,001,694
Trainable params: 10,001,694
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/75
1712/1712 [==============================] - 66s - loss: 0.0397 - val_loss: 0.0071
Epoch 2/75
1712/1712 [==============================] - 0s - loss: 0.0107 - val_loss: 0.0043
Epoch 3/75
1712/1712 [==============================] - 0s - loss: 0.0085 - val_loss: 0.0046
Epoch 4/75
1712/1712 [==============================] - 0s - loss: 0.0068 - val_loss: 0.0038
Epoch 5/75
1712/1712 [==============================] - 0s - loss: 0.0060 - val_loss: 0.0044
Epoch 6/75
1712/1712 [==============================] - 0s - loss: 0.0054 - val_loss: 0.0033
Epoch 7/75
1712/1712 [==============================] - 0s - loss: 0.0049 - val_loss: 0.0031
Epoch 8/75
1712/1712 [==============================] - 0s - loss: 0.0046 - val_loss: 0.0028
Epoch 9/75
1712/1712 [==============================] - 0s - loss: 0.0040 - val_loss: 0.0025
Epoch 10/75
1712/1712 [==============================] - 0s - loss: 0.0039 - val_loss: 0.0024
Epoch 11/75
1712/1712 [==============================] - 0s - loss: 0.0035 - val_loss: 0.0022
Epoch 12/75
1712/1712 [==============================] - 0s - loss: 0.0033 - val_loss: 0.0021
Epoch 13/75
1712/1712 [==============================] - 0s - loss: 0.0030 - val_loss: 0.0020
Epoch 14/75
1712/1712 [==============================] - 0s - loss: 0.0028 - val_loss: 0.0019
Epoch 15/75
1712/1712 [==============================] - 0s - loss: 0.0027 - val_loss: 0.0019
Epoch 16/75
1712/1712 [==============================] - 0s - loss: 0.0026 - val_loss: 0.0019
Epoch 17/75
1712/1712 [==============================] - 0s - loss: 0.0024 - val_loss: 0.0017
Epoch 18/75
1712/1712 [==============================] - 0s - loss: 0.0023 - val_loss: 0.0016
Epoch 19/75
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0017
Epoch 20/75
1712/1712 [==============================] - 0s - loss: 0.0022 - val_loss: 0.0015
Epoch 21/75
1712/1712 [==============================] - 0s - loss: 0.0021 - val_loss: 0.0014
Epoch 22/75
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0015
Epoch 23/75
1712/1712 [==============================] - 0s - loss: 0.0020 - val_loss: 0.0015
Epoch 24/75
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0015
Epoch 25/75
1712/1712 [==============================] - 0s - loss: 0.0019 - val_loss: 0.0014
Epoch 26/75
1712/1712 [==============================] - 0s - loss: 0.0018 - val_loss: 0.0013
Epoch 27/75
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0013
Epoch 28/75
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0013
Epoch 29/75
1712/1712 [==============================] - 0s - loss: 0.0017 - val_loss: 0.0015
Epoch 30/75
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0014
Epoch 31/75
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 32/75
1712/1712 [==============================] - 0s - loss: 0.0016 - val_loss: 0.0013
Epoch 33/75
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0013
Epoch 34/75
1712/1712 [==============================] - 0s - loss: 0.0015 - val_loss: 0.0013
Epoch 35/75
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0013
Epoch 36/75
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0012
Epoch 37/75
1712/1712 [==============================] - 0s - loss: 0.0014 - val_loss: 0.0013
Epoch 38/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 39/75
1712/1712 [==============================] - 2s - loss: 0.0013 - val_loss: 0.0013
Epoch 40/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 41/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0014
Epoch 42/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 43/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 44/75
1712/1712 [==============================] - 0s - loss: 0.0013 - val_loss: 0.0012
Epoch 45/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0013
Epoch 46/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 47/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 48/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 49/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0013
Epoch 50/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 51/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 52/75
1712/1712 [==============================] - 0s - loss: 0.0012 - val_loss: 0.0012
Epoch 53/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0011
Epoch 54/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0012
Epoch 55/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0011
Epoch 56/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0011
Epoch 57/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0012
Epoch 58/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0012
Epoch 59/75
1712/1712 [==============================] - 0s - loss: 0.0011 - val_loss: 0.0013
Epoch 60/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0012
Epoch 61/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0012
Epoch 62/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0012
Epoch 63/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0011
Epoch 64/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0012
Epoch 65/75
1712/1712 [==============================] - 0s - loss: 0.0010 - val_loss: 0.0012
Epoch 66/75
1712/1712 [==============================] - 0s - loss: 9.6412e-04 - val_loss: 0.0012
Epoch 67/75
1712/1712 [==============================] - 0s - loss: 9.6840e-04 - val_loss: 0.0011
Epoch 68/75
1712/1712 [==============================] - 0s - loss: 9.4940e-04 - val_loss: 0.0012
Epoch 69/75
1712/1712 [==============================] - 0s - loss: 9.8126e-04 - val_loss: 0.0011
Epoch 70/75
1712/1712 [==============================] - 0s - loss: 9.5810e-04 - val_loss: 0.0011
Epoch 71/75
1712/1712 [==============================] - 0s - loss: 9.3326e-04 - val_loss: 0.0012
Epoch 72/75
1712/1712 [==============================] - 0s - loss: 9.1542e-04 - val_loss: 0.0011
Epoch 73/75
1712/1712 [==============================] - 0s - loss: 8.9076e-04 - val_loss: 0.0012
Epoch 74/75
1712/1712 [==============================] - 0s - loss: 9.0800e-04 - val_loss: 0.0011
Epoch 75/75
1712/1712 [==============================] - 0s - loss: 8.8754e-04 - val_loss: 0.0011
In [24]:
fig = plt.figure(figsize = (10, 10))

ax1 = fig.add_subplot(111)
ax1.set_ylim(0, 0.02)
ax1.set_title('Final model 75 epochs - optimizer: adam')
ax1.set_ylabel('loss')
ax1.plot(hist.history['loss'])
ax1.plot(hist.history['val_loss'])
Out[24]:
[<matplotlib.lines.Line2D at 0x21bbba35208>]

Question 3: Do you notice any evidence of overfitting or underfitting in the above plot? If so, what steps have you taken to improve your model? Note that slight overfitting or underfitting will not hurt your chances of a successful submission, as long as you have attempted some solutions towards improving your model (such as regularization, dropout, increased/decreased number of layers, etc).

Answer: There is still slight overfitting on the final model – however, I think it is acceptable in comparison to simple model without dropout technique (take a look at graph above: "Simple model - optimizer adam"). This simple model suffers from heavy overfitting as validation loss stop decreasing but training loss is quickly heading to zero. Actually, final model was an evolution of this simple model by adding additional layer and using dropout technique to fight overfitting. Additionally, more complex model was built in attempt to reduce overfitting even more ("Complex model"). Actually, as we can see from graph "Complex model - optimizer adam") is still not overfit as training loss higher than validation loss. So, I've made attempt to see if this complex model can achieve better performance with more epochs. Tying 300 epochs, I found out that it starts to overfit at 150 epochs and validation loss still a bit higher than loss for the "Intermediate model - adam optimizer” - so this one was selected as final model.

Visualize a Subset of the Test Predictions

Execute the code cell below to visualize your model's predicted keypoints on a subset of the testing images.

In [25]:
y_test = model.predict(X_test)
shift = 0
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(shift, shift + 9):
    ax = fig.add_subplot(3, 3, i + 1 - shift, xticks=[], yticks=[])
    plot_data(X_test[i], y_test[i], ax)

Step 8: Complete the pipeline

With the work you did in Sections 1 and 2 of this notebook, along with your freshly trained facial keypoint detector, you can now complete the full pipeline. That is given a color image containing a person or persons you can now

  • Detect the faces in this image automatically using OpenCV
  • Predict the facial keypoints in each face detected in the image
  • Paint predicted keypoints on each face detected

In this Subsection you will do just this!

(IMPLEMENTATION) Facial Keypoints Detector

Use the OpenCV face detection functionality you built in previous Sections to expand the functionality of your keypoints detector to color images with arbitrary size. Your function should perform the following steps

  1. Accept a color image.
  2. Convert the image to grayscale.
  3. Detect and crop the face contained in the image.
  4. Locate the facial keypoints in the cropped image.
  5. Overlay the facial keypoints in the original (color, uncropped) image.

Note: step 4 can be the trickiest because remember your convolutional network is only trained to detect facial keypoints in $96 \times 96$ grayscale images where each pixel was normalized to lie in the interval $[0,1]$, and remember that each facial keypoint was normalized during training to the interval $[-1,1]$. This means - practically speaking - to paint detected keypoints onto a test face you need to perform this same pre-processing to your candidate face - that is after detecting it you should resize it to $96 \times 96$ and normalize its values before feeding it into your facial keypoint detector. To be shown correctly on the original image the output keypoints from your detector then need to be shifted and re-normalized from the interval $[-1,1]$ to the width and height of your detected face.

When complete you should be able to produce example images like the one below

In [26]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')


# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image)
Out[26]:
<matplotlib.image.AxesImage at 0x21bb6e8e160>
In [27]:
### TODO: Use the face detection code we saw in Section 1 with your trained conv-net 
## TODO : Paint the predicted keypoints on the test image
image_size = 96

def convert_to_gray(image):
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    return gray

def detect_keypoints(image, gray, regions):
    image_copy = np.copy(image)

    for (x, y, w, h) in regions:
        region = np.copy(gray[y : y + h, x : x + w])
        resized_image = cv2.resize(region, (image_size, image_size)) 
        resized_image = resized_image.reshape(1, image_size, image_size, 1)
        resized_image = resized_image / 255
        points = model.predict(resized_image)
        points = points.flatten()
        points = points * (image_size // 2) + image_size // 2 
           
        points[::2] = points[::2] / image_size  * region.shape[1]
        points[1::2] = points[1::2] / image_size  * region.shape[0]
        
        for i in range(0, len(points), 2):
            cv2.circle(image_copy, (x + int(points[i]), y + int(points[i+1])), 3, (0, 255, 0), -1)
        
        #blured_image[y : y + h, x : x + w, :] = blured_region 

    return image_copy

def processing_pipeline(image):
    gray = convert_to_gray(image)
    detector = get_face_detector()
    faces = face_cascade.detectMultiScale(gray, 2, 6)
    for (x,y,w,h) in faces:
        cv2.rectangle(image, (x, y), (x+w,y+h), (255,0,0), 3)
    res = detect_keypoints(image, gray, faces)
    
    return res

#plt.imshow(gray, cmap='gray')
#plt.show()

# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

image_copy = processing_pipeline(image)

# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image_copy)
Out[27]:
<matplotlib.image.AxesImage at 0x21bb6da1748>

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add facial keypoint detection to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for keypoint detection and marking in the previous exercise and you should be good to go!

In [28]:
import cv2
import time 
from keras.models import load_model
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # keep video stream open
    while rval:
        # plot image from camera with detections marked
        frame = processing_pipeline(frame)
        cv2.imshow("face detection activated", frame)
        
        # exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key == 27: # exit by pressing any key
            # destroy windows
            cv2.destroyAllWindows()
            
            # hack from stack overflow for making sure window closes on osx --> https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()  
In [29]:
# Run your keypoint face painter
laptop_camera_go()

(Optional) Further Directions - add a filter using facial keypoints

Using your freshly minted facial keypoint detector pipeline you can now do things like add fun filters to a person's face automatically. In this optional exercise you can play around with adding sunglasses automatically to each individual's face in an image as shown in a demonstration image below.

To produce this effect an image of a pair of sunglasses shown in the Python cell below.

In [30]:
# Load in sunglasses image - note the usage of the special option
# cv2.IMREAD_UNCHANGED, this option is used because the sunglasses 
# image has a 4th channel that allows us to control how transparent each pixel in the image is
sunglasses = cv2.imread("images/sunglasses_4.png", cv2.IMREAD_UNCHANGED)

# Plot the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.imshow(sunglasses)
ax1.axis('off');

This image is placed over each individual's face using the detected eye points to determine the location of the sunglasses, and eyebrow points to determine the size that the sunglasses should be for each person (one could also use the nose point to determine this).

Notice that this image actually has 4 channels, not just 3.

In [31]:
# Print out the shape of the sunglasses image
print ('The sunglasses image has shape: ' + str(np.shape(sunglasses)))
The sunglasses image has shape: (1123, 3064, 4)

It has the usual red, blue, and green channels any color image has, with the 4th channel representing the transparency level of each pixel in the image. Here's how the transparency channel works: the lower the value, the more transparent the pixel will become. The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen.

This is how we can place this image of sunglasses on someone's face and still see the area around of their face where the sunglasses lie - because these pixels in the sunglasses image have been made completely transparent.

Lets check out the alpha channel of our sunglasses image in the next Python cell. Note because many of the pixels near the boundary are transparent we'll need to explicitly print out non-zero values if we want to see them.

In [32]:
# Print out the sunglasses transparency (alpha) channel
alpha_channel = sunglasses[:,:,3]
print ('the alpha channel here looks like')
print (alpha_channel)

# Just to double check that there are indeed non-zero values
# Let's find and print out every value greater than zero
values = np.where(alpha_channel != 0)
print ('\n the non-zero values of the alpha channel look like')
print (values)
the alpha channel here looks like
[[0 0 0 ..., 0 0 0]
 [0 0 0 ..., 0 0 0]
 [0 0 0 ..., 0 0 0]
 ..., 
 [0 0 0 ..., 0 0 0]
 [0 0 0 ..., 0 0 0]
 [0 0 0 ..., 0 0 0]]

 the non-zero values of the alpha channel look like
(array([  17,   17,   17, ..., 1109, 1109, 1109], dtype=int64), array([ 687,  688,  689, ..., 2376, 2377, 2378], dtype=int64))

This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter to tell us which pixels to overlay on a new image (only the non-transparent ones with values greater than zero).

One last thing: it's helpful to understand which keypoint belongs to the eyes, mouth, etc. So, in the image below, we also display the index of each facial keypoint directly on the image so that you can tell which keypoints are for the eyes, eyebrows, etc.

With this information, you're well on your way to completing this filtering task! See if you can place the sunglasses automatically on the individuals in the image loaded in / shown in the next Python cell.

In [33]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot the image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[33]:
<matplotlib.image.AxesImage at 0x21bb6e5f4e0>
In [37]:
## (Optional) TODO: Use the face detection code we saw in Section 1 with your trained conv-net to put
## sunglasses on the individuals in our test image
def detect_keypoints(image, gray, regions):
    image_copy = np.copy(image)
    
    res = []
    
    for (x, y, w, h) in regions:
        region = np.copy(gray[y : y + h, x : x + w])
        resized_image = cv2.resize(region, (image_size, image_size)) 
        resized_image = resized_image.reshape(1, image_size, image_size, 1)
        resized_image = resized_image / 255
        points = model.predict(resized_image)
        points = points.flatten()
        points = points * (image_size // 2) + image_size // 2 
           
        points[::2] = points[::2] / image_size  * region.shape[1]
        points[1::2] = points[1::2] / image_size  * region.shape[0]
        
        res.append(((x,y), points))
        
    return res

def put_sunglasses(image, overlay, points_set):
    image_copy = np.copy(image)
    
    for face, points in points_set:
        start_x = points[2 * 9]
        start_y = points[2 * 9 + 1]
        end_x = points[2 * 7]
        end_y = points[2 * 7 + 1]
        
        x_size = end_x - start_x 
        
        overlay_ratio = overlay.shape[1] / overlay.shape[0] 
        overlay_resized = cv2.resize(overlay, (x_size, int(x_size / overlay_ratio))) 

        y1, y2 = int(start_y), int(start_y + overlay_resized.shape[0])
        x1, x2 = int(start_x), int(start_x + overlay_resized.shape[1])

        alpha = overlay_resized[:, :, 3] / 255.0
    
        for c in range(0, 3):
            image_copy[face[1] + y1 : face[1] + y2, face[0] + x1 : face[0] + x2, c] = (alpha * overlay_resized[:, :, c] +
                                  (1.0 - alpha) * image_copy[face[1] + y1 : face[1] + y2, face[0] + x1 : face[0] + x2, c])
    
    return image_copy

def filter_pipeline(image, overlay):
    gray = convert_to_gray(image)
    detector = get_face_detector()
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    points_set = detect_keypoints(image, gray, faces)
    res = put_sunglasses(image, overlay, points_set)
    return res

image_copy = filter_pipeline(image, sunglasses)

# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image_copy)
Out[37]:
<matplotlib.image.AxesImage at 0x2207f571668>

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add the sunglasses filter to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for adding sunglasses to someone's face in the previous optional exercise and you should be good to go!

In [38]:
import cv2
import time 
from keras.models import load_model
import numpy as np

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        frame = filter_pipeline(frame, sunglasses)
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key == 27: # exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [39]:
# Load facial landmark detector model
model = load_model('my_model.h5')

# Run sunglasses painter
laptop_camera_go()
In [ ]: